From a31c19ab414558ba5eeda10999e0964b8e86d473 Mon Sep 17 00:00:00 2001
From: Amandine Croset <croset.amandine@gmail.com>
Date: Sun, 15 Oct 2023 20:56:36 +0200
Subject: [PATCH] AmandineCroset

---
 .DS_Store                                     | Bin 0 -> 6148 bytes
 your-code/.DS_Store                           | Bin 0 -> 6148 bytes
 .../.ipynb_checkpoints/main-checkpoint.ipynb  | 469 +++++++++++
 your-code/main.ipynb                          | 730 +++++++++++-------
 4 files changed, 938 insertions(+), 261 deletions(-)
 create mode 100644 .DS_Store
 create mode 100644 your-code/.DS_Store
 create mode 100644 your-code/.ipynb_checkpoints/main-checkpoint.ipynb

diff --git a/.DS_Store b/.DS_Store
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diff --git a/your-code/.DS_Store b/your-code/.DS_Store
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diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..04b18a7
--- /dev/null
+++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb
@@ -0,0 +1,469 @@
+{
+ "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": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "series = pd.Series(lst)\n",
+    "print(series)"
+   ]
+  },
+  {
+   "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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "third_value = series[2]\n",
+    "print(third_value)"
+   ]
+  },
+  {
+   "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": 8,
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      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\n"
+     ]
+    }
+   ],
+   "source": [
+    "b_dataframe = pd.DataFrame(b)\n",
+    "print(b_dataframe)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Rename the data frame columns based on the names in the list below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames = ['Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   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\n"
+     ]
+    }
+   ],
+   "source": [
+    "b_dataframe = b_dataframe.rename(columns={0:'Score_1',1:'Score_2',2:'Score_3',3:'Score_4',4:'Score_5'})\n",
+    "print(b_dataframe)"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   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\n"
+     ]
+    }
+   ],
+   "source": [
+    "subset = b_dataframe[['Score_1', 'Score_3', 'Score_5']]\n",
+    "print(subset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. From the original data frame, calculate the average Score_3 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "56.95\n"
+     ]
+    }
+   ],
+   "source": [
+    "average = b_dataframe['Score_3'].mean()\n",
+    "print(f\"{average:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8. From the original data frame, calculate the maximum Score_4 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "88.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_value = b_dataframe['Score_4'].max()\n",
+    "print(max_value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9. From the original data frame, calculate the median Score 2 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40.75\n"
+     ]
+    }
+   ],
+   "source": [
+    "median = b_dataframe['Score_2'].median()\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 10. Create a Pandas DataFrame from the dictionary of product orders below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          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\n"
+     ]
+    }
+   ],
+   "source": [
+    "orders_df = pd.DataFrame(orders)\n",
+    "print(orders_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 11. Calculate the total quantity ordered and revenue generated from these orders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2978\n",
+      "637.0\n",
+      "637.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Total quantity\n",
+    "total_quantity = orders_df['Quantity'].sum()\n",
+    "print(total_quantity)\n",
+    "\n",
+    "# Total revenue\n",
+    "total_revenue = orders_df['Revenue'].sum()\n",
+    "print(total_revenue)\n",
+    "\n",
+    "total_revenue_2 = (orders_df['Quantity'] * orders_df['UnitPrice']).sum()\n",
+    "print(total_revenue_2)"
+   ]
+  },
+  {
+   "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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11.95\n",
+      "0.18\n"
+     ]
+    }
+   ],
+   "source": [
+    "most_expensive_price = orders_df['UnitPrice'].max()\n",
+    "print(most_expensive_price)\n",
+    "\n",
+    "least_expensive_price = orders_df['UnitPrice'].min()\n",
+    "print(least_expensive_price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "def difference_prices (max,min):\n",
+    "    result = max-min\n",
+    "    return result\n",
+    "print(difference_prices(most_expensive_price, least_expensive_price))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 8a76302..04b18a7 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,261 +1,469 @@
-{
- "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. Create a Pandas Series containing the elements of the list below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "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": null,
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 5. Rename the data frame columns based on the names in the list below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colnames = ['Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 7. From the original data frame, calculate the average Score_3 value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 8. From the original data frame, calculate the maximum Score_4 value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 9. From the original data frame, calculate the median Score 2 value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 10. Create a Pandas DataFrame from the dictionary of product orders below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 11. Calculate the total quantity ordered and revenue generated from these orders."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "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": 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": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "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": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "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\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "series = pd.Series(lst)\n",
+    "print(series)"
+   ]
+  },
+  {
+   "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": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "third_value = series[2]\n",
+    "print(third_value)"
+   ]
+  },
+  {
+   "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": 8,
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      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\n"
+     ]
+    }
+   ],
+   "source": [
+    "b_dataframe = pd.DataFrame(b)\n",
+    "print(b_dataframe)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Rename the data frame columns based on the names in the list below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames = ['Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   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\n"
+     ]
+    }
+   ],
+   "source": [
+    "b_dataframe = b_dataframe.rename(columns={0:'Score_1',1:'Score_2',2:'Score_3',3:'Score_4',4:'Score_5'})\n",
+    "print(b_dataframe)"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   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\n"
+     ]
+    }
+   ],
+   "source": [
+    "subset = b_dataframe[['Score_1', 'Score_3', 'Score_5']]\n",
+    "print(subset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. From the original data frame, calculate the average Score_3 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "56.95\n"
+     ]
+    }
+   ],
+   "source": [
+    "average = b_dataframe['Score_3'].mean()\n",
+    "print(f\"{average:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8. From the original data frame, calculate the maximum Score_4 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "88.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_value = b_dataframe['Score_4'].max()\n",
+    "print(max_value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9. From the original data frame, calculate the median Score 2 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40.75\n"
+     ]
+    }
+   ],
+   "source": [
+    "median = b_dataframe['Score_2'].median()\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 10. Create a Pandas DataFrame from the dictionary of product orders below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                          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\n"
+     ]
+    }
+   ],
+   "source": [
+    "orders_df = pd.DataFrame(orders)\n",
+    "print(orders_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 11. Calculate the total quantity ordered and revenue generated from these orders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2978\n",
+      "637.0\n",
+      "637.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Total quantity\n",
+    "total_quantity = orders_df['Quantity'].sum()\n",
+    "print(total_quantity)\n",
+    "\n",
+    "# Total revenue\n",
+    "total_revenue = orders_df['Revenue'].sum()\n",
+    "print(total_revenue)\n",
+    "\n",
+    "total_revenue_2 = (orders_df['Quantity'] * orders_df['UnitPrice']).sum()\n",
+    "print(total_revenue_2)"
+   ]
+  },
+  {
+   "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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11.95\n",
+      "0.18\n"
+     ]
+    }
+   ],
+   "source": [
+    "most_expensive_price = orders_df['UnitPrice'].max()\n",
+    "print(most_expensive_price)\n",
+    "\n",
+    "least_expensive_price = orders_df['UnitPrice'].min()\n",
+    "print(least_expensive_price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "def difference_prices (max,min):\n",
+    "    result = max-min\n",
+    "    return result\n",
+    "print(difference_prices(most_expensive_price, least_expensive_price))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}