your-code/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Pandas Lab\n",
    "\n",
    "Complete the following set of exercises to solidify your knowledge of Pandas fundamentals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Numpy and Pandas and alias them to `np` and `pd` respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create a Pandas Series containing the elements of the list below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [5.7, 75.2, 74.4, 84.0, 66.5, 66.3, 55.8, 75.7, 29.1, 43.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <td>5.7</td>\n",
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       "      <td>75.2</td>\n",
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       "      <td>74.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>75.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
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       "      <th>9</th>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0\n",
       "0   5.7\n",
       "1  75.2\n",
       "2  74.4\n",
       "3  84.0\n",
       "4  66.5\n",
       "5  66.3\n",
       "6  55.8\n",
       "7  75.7\n",
       "8  29.1\n",
       "9  43.7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame(lst)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Use indexing to return the third value in the Series above.\n",
    "\n",
    "*Hint: Remember that indexing begins at 0.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      0\n",
       "2  74.4"
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     "metadata": {},
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   "source": [
    "data[2:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Create a Pandas DataFrame from the list of lists below. Each sublist should be represented as a row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = [[53.1, 95.0, 67.5, 35.0, 78.4],\n",
    "     [61.3, 40.8, 30.8, 37.8, 87.6],\n",
    "     [20.6, 73.2, 44.2, 14.6, 91.8],\n",
    "     [57.4, 0.1, 96.1, 4.2, 69.5],\n",
    "     [83.6, 20.5, 85.4, 22.8, 35.9],\n",
    "     [49.0, 69.0, 0.1, 31.8, 89.1],\n",
    "     [23.3, 40.7, 95.0, 83.8, 26.9],\n",
    "     [27.6, 26.4, 53.8, 88.8, 68.5],\n",
    "     [96.6, 96.4, 53.4, 72.4, 50.1],\n",
    "     [73.7, 39.0, 43.2, 81.6, 34.7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>1</th>\n",
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       "      <td>69.5</td>\n",
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       "      <td>49.0</td>\n",
       "      <td>69.0</td>\n",
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       "      <td>31.8</td>\n",
       "      <td>89.1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>40.7</td>\n",
       "      <td>95.0</td>\n",
       "      <td>83.8</td>\n",
       "      <td>26.9</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>53.8</td>\n",
       "      <td>88.8</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.6</td>\n",
       "      <td>96.4</td>\n",
       "      <td>53.4</td>\n",
       "      <td>72.4</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>81.6</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "      0     1     2     3     4\n",
       "0  53.1  95.0  67.5  35.0  78.4\n",
       "1  61.3  40.8  30.8  37.8  87.6\n",
       "2  20.6  73.2  44.2  14.6  91.8\n",
       "3  57.4   0.1  96.1   4.2  69.5\n",
       "4  83.6  20.5  85.4  22.8  35.9\n",
       "5  49.0  69.0   0.1  31.8  89.1\n",
       "6  23.3  40.7  95.0  83.8  26.9\n",
       "7  27.6  26.4  53.8  88.8  68.5\n",
       "8  96.6  96.4  53.4  72.4  50.1\n",
       "9  73.7  39.0  43.2  81.6  34.7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list=pd.DataFrame(b)\n",
    "data_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Rename the data frame columns based on the names in the list below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames = ['Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <th>5</th>\n",
       "      <td>49.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>89.1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>40.7</td>\n",
       "      <td>95.0</td>\n",
       "      <td>83.8</td>\n",
       "      <td>26.9</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.6</td>\n",
       "      <td>26.4</td>\n",
       "      <td>53.8</td>\n",
       "      <td>88.8</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.6</td>\n",
       "      <td>96.4</td>\n",
       "      <td>53.4</td>\n",
       "      <td>72.4</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>81.6</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Score_1  Score_2  Score_3  Score_4  Score_5\n",
       "0     53.1     95.0     67.5     35.0     78.4\n",
       "1     61.3     40.8     30.8     37.8     87.6\n",
       "2     20.6     73.2     44.2     14.6     91.8\n",
       "3     57.4      0.1     96.1      4.2     69.5\n",
       "4     83.6     20.5     85.4     22.8     35.9\n",
       "5     49.0     69.0      0.1     31.8     89.1\n",
       "6     23.3     40.7     95.0     83.8     26.9\n",
       "7     27.6     26.4     53.8     88.8     68.5\n",
       "8     96.6     96.4     53.4     72.4     50.1\n",
       "9     73.7     39.0     43.2     81.6     34.7"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_list.columns=colnames\n",
    "data_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Create a subset of this data frame that contains only the Score 1, 3, and 5 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Score_1</th>\n",
       "      <th>Score_3</th>\n",
       "      <th>Score_5</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>61.3</td>\n",
       "      <td>30.8</td>\n",
       "      <td>87.6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.6</td>\n",
       "      <td>44.2</td>\n",
       "      <td>91.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57.4</td>\n",
       "      <td>96.1</td>\n",
       "      <td>69.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>83.6</td>\n",
       "      <td>85.4</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>89.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.3</td>\n",
       "      <td>95.0</td>\n",
       "      <td>26.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.6</td>\n",
       "      <td>53.8</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>96.6</td>\n",
       "      <td>53.4</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>73.7</td>\n",
       "      <td>43.2</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score_1  Score_3  Score_5\n",
       "0     53.1     67.5     78.4\n",
       "1     61.3     30.8     87.6\n",
       "2     20.6     44.2     91.8\n",
       "3     57.4     96.1     69.5\n",
       "4     83.6     85.4     35.9\n",
       "5     49.0      0.1     89.1\n",
       "6     23.3     95.0     26.9\n",
       "7     27.6     53.8     68.5\n",
       "8     96.6     53.4     50.1\n",
       "9     73.7     43.2     34.7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list_135=data_list[[\"Score_1\",\"Score_3\",\"Score_5\"]]\n",
    "data_list_135"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. From the original data frame, calculate the average Score_3 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.95000000000001\n"
     ]
    }
   ],
   "source": [
    "Score3_avg=np.average(data_list[\"Score_3\"].values)\n",
    "print(Score3_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. From the original data frame, calculate the maximum Score_4 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88.8\n"
     ]
    }
   ],
   "source": [
    "Score4_max=np.max(data_list[\"Score_4\"].values)\n",
    "print(Score4_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. From the original data frame, calculate the median Score 2 value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40.75\n"
     ]
    }
   ],
   "source": [
    "Score2_med=np.median(data_list[\"Score_2\"].values)\n",
    "print(Score2_med)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Create a Pandas DataFrame from the dictionary of product orders below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = {'Description': ['LUNCH BAG APPLE DESIGN',\n",
    "  'SET OF 60 VINTAGE LEAF CAKE CASES ',\n",
    "  'RIBBON REEL STRIPES DESIGN ',\n",
    "  'WORLD WAR 2 GLIDERS ASSTD DESIGNS',\n",
    "  'PLAYING CARDS JUBILEE UNION JACK',\n",
    "  'POPCORN HOLDER',\n",
    "  'BOX OF VINTAGE ALPHABET BLOCKS',\n",
    "  'PARTY BUNTING',\n",
    "  'JAZZ HEARTS ADDRESS BOOK',\n",
    "  'SET OF 4 SANTA PLACE SETTINGS'],\n",
    " 'Quantity': [1, 24, 1, 2880, 2, 7, 1, 4, 10, 48],\n",
    " 'UnitPrice': [1.65, 0.55, 1.65, 0.18, 1.25, 0.85, 11.95, 4.95, 0.19, 1.25],\n",
    " 'Revenue': [1.65, 13.2, 1.65, 518.4, 2.5, 5.95, 11.95, 19.8, 1.9, 60.0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LUNCH BAG APPLE DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SET OF 60 VINTAGE LEAF CAKE CASES</td>\n",
       "      <td>24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIBBON REEL STRIPES DESIGN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WORLD WAR 2 GLIDERS ASSTD DESIGNS</td>\n",
       "      <td>2880</td>\n",
       "      <td>0.18</td>\n",
       "      <td>518.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PLAYING CARDS JUBILEE UNION JACK</td>\n",
       "      <td>2</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>POPCORN HOLDER</td>\n",
       "      <td>7</td>\n",
       "      <td>0.85</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BOX OF VINTAGE ALPHABET BLOCKS</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>11.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PARTY BUNTING</td>\n",
       "      <td>4</td>\n",
       "      <td>4.95</td>\n",
       "      <td>19.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JAZZ HEARTS ADDRESS BOOK</td>\n",
       "      <td>10</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SET OF 4 SANTA PLACE SETTINGS</td>\n",
       "      <td>48</td>\n",
       "      <td>1.25</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Description  Quantity  UnitPrice  Revenue\n",
       "0              LUNCH BAG APPLE DESIGN         1       1.65     1.65\n",
       "1  SET OF 60 VINTAGE LEAF CAKE CASES         24       0.55    13.20\n",
       "2         RIBBON REEL STRIPES DESIGN          1       1.65     1.65\n",
       "3   WORLD WAR 2 GLIDERS ASSTD DESIGNS      2880       0.18   518.40\n",
       "4    PLAYING CARDS JUBILEE UNION JACK         2       1.25     2.50\n",
       "5                      POPCORN HOLDER         7       0.85     5.95\n",
       "6      BOX OF VINTAGE ALPHABET BLOCKS         1      11.95    11.95\n",
       "7                       PARTY BUNTING         4       4.95    19.80\n",
       "8            JAZZ HEARTS ADDRESS BOOK        10       0.19     1.90\n",
       "9       SET OF 4 SANTA PLACE SETTINGS        48       1.25    60.00"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders=pd.DataFrame(orders)\n",
    "df_orders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Calculate the total quantity ordered and revenue generated from these orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total 2978 orders with total revenue of 637.0\n"
     ]
    }
   ],
   "source": [
    "sum_orders=np.sum(df_orders[\"Quantity\"].values)\n",
    "rev_orders=np.sum(df_orders[\"Revenue\"].values)\n",
    "print(\"Total\",sum_orders,\"orders\",\"with total revenue of\",rev_orders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Obtain the prices of the most expensive and least expensive items ordered and print the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.77\n"
     ]
    }
   ],
   "source": [
    "max_price=np.max(df_orders[\"UnitPrice\"].values)\n",
    "min_price=np.min(df_orders[\"UnitPrice\"].values)\n",
    "print(max_price-min_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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