[lab-intro-pandas]AlexanderGroenert

your-code/.ipynb_checkpoints/main-checkpoint.ipynb

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+{
+ "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": []
+  }
+ ],
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+  },
+  "language_info": {
+   "codemirror_mode": {
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+   "pygments_lexer": "ipython3",
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+}