diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..7eb8003 --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,952 @@ +{ + "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": 2, + "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": [ + { + "data": { + "text/html": [ + "
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" + ], + "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": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_b_2 = data_b[[\"Score_1\",\"Score_3\",\"Score_5\"]]\n", + "data_b_2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 7. From the original data frame, calculate the average Score_3 value." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "56.95000000000001" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_b[\"Score_3\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 8. From the original data frame, calculate the maximum Score_4 value." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "88.8" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_b[\"Score_4\"].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 9. From the original data frame, calculate the median Score 2 value." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40.75" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_b[\"Score_2\"].median()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 10. Create a Pandas DataFrame from the dictionary of product orders below." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DescriptionQuantityUnitPriceRevenue
0LUNCH BAG APPLE DESIGN11.651.65
1SET OF 60 VINTAGE LEAF CAKE CASES240.5513.20
2RIBBON REEL STRIPES DESIGN11.651.65
3WORLD WAR 2 GLIDERS ASSTD DESIGNS28800.18518.40
4PLAYING CARDS JUBILEE UNION JACK21.252.50
5POPCORN HOLDER70.855.95
6BOX OF VINTAGE ALPHABET BLOCKS111.9511.95
7PARTY BUNTING44.9519.80
8JAZZ HEARTS ADDRESS BOOK100.191.90
9SET OF 4 SANTA PLACE SETTINGS481.2560.00
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" + ], + "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": 26, + "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": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978 637.0\n" + ] + } + ], + "source": [ + "print(df_orders[\"Quantity\"].sum(), df_orders[\"Revenue\"].sum())" + ] + }, + { + "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": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.77\n" + ] + } + ], + "source": [ + "print(df_orders[\"UnitPrice\"].max() - df_orders[\"UnitPrice\"].min())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 03f020a..7eb8003 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -18,10 +18,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] }, { "cell_type": "markdown", @@ -32,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -41,10 +44,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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DescriptionQuantityUnitPriceRevenue
0LUNCH BAG APPLE DESIGN11.651.65
1SET OF 60 VINTAGE LEAF CAKE CASES240.5513.20
2RIBBON REEL STRIPES DESIGN11.651.65
3WORLD WAR 2 GLIDERS ASSTD DESIGNS28800.18518.40
4PLAYING CARDS JUBILEE UNION JACK21.252.50
5POPCORN HOLDER70.855.95
6BOX OF VINTAGE ALPHABET BLOCKS111.9511.95
7PARTY BUNTING44.9519.80
8JAZZ HEARTS ADDRESS BOOK100.191.90
9SET OF 4 SANTA PLACE SETTINGS481.2560.00
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" + ], + "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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_orders = pd.DataFrame(orders)\n", + "df_orders" + ] }, { "cell_type": "markdown", @@ -217,10 +881,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978 637.0\n" + ] + } + ], + "source": [ + "print(df_orders[\"Quantity\"].sum(), df_orders[\"Revenue\"].sum())" + ] }, { "cell_type": "markdown", @@ -229,6 +903,23 @@ "### 12. Obtain the prices of the most expensive and least expensive items ordered and print the difference." ] }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.77\n" + ] + } + ], + "source": [ + "print(df_orders[\"UnitPrice\"].max() - df_orders[\"UnitPrice\"].min())" + ] + }, { "cell_type": "code", "execution_count": null, @@ -239,7 +930,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -253,7 +944,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.10.2" } }, "nbformat": 4,