diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 0fc1af6..4367f90 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -18,10 +18,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] }, { "cell_type": "markdown", @@ -32,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -41,10 +44,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], - "source": [] + "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": [ + "s = pd.Series(lst)\n", + "\n", + "print(s)" + ] }, { "cell_type": "markdown", @@ -57,10 +82,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "74.4\n" + ] + } + ], + "source": [ + "third_value = s[2]\n", + "\n", + "print(third_value)" + ] }, { "cell_type": "markdown", @@ -71,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -89,10 +126,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], - "source": [] + "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": [ + "df = pd.DataFrame(b)\n", + "\n", + "print(df)" + ] }, { "cell_type": "markdown", @@ -103,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -112,10 +171,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], - "source": [] + "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": [ + "df = df.rename(columns=dict(zip(df.columns, colnames)))\n", + "\n", + "print(df)" + ] }, { "cell_type": "markdown", @@ -126,10 +207,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], - "source": [] + "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 = df[['Score_1', 'Score_3', 'Score_5']]\n", + "\n", + "print(subset)" + ] }, { "cell_type": "markdown", @@ -140,10 +243,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.95000000000001\n" + ] + } + ], + "source": [ + "average_score_3 = df['Score_3'].mean()\n", + "\n", + "print(average_score_3)" + ] }, { "cell_type": "markdown", @@ -154,10 +269,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88.8\n" + ] + } + ], + "source": [ + "max_score_4 = df['Score_4'].max()\n", + "\n", + "print(max_score_4)" + ] }, { "cell_type": "markdown", @@ -168,10 +295,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.75\n" + ] + } + ], + "source": [ + "median_score_2 = df['Score_2'].median()\n", + "\n", + "print(median_score_2)" + ] }, { "cell_type": "markdown", @@ -182,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -203,10 +342,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], - "source": [] + "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": [ + "df = pd.DataFrame(orders)\n", + "\n", + "print(df)" + ] }, { "cell_type": "markdown", @@ -217,10 +378,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total quantity ordered: 2978\n", + "Total revenue generated: 637.0\n" + ] + } + ], + "source": [ + "total_quantity = df['Quantity'].sum()\n", + "total_revenue = df['Revenue'].sum()\n", + "print('Total quantity ordered:', total_quantity)\n", + "print('Total revenue generated:', total_revenue)" + ] }, { "cell_type": "markdown", @@ -229,6 +404,31 @@ "### 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": [ + "Most expensive item price: 11.95\n", + "Least expensive item price: 0.18\n", + "Price difference: 11.77\n" + ] + } + ], + "source": [ + "most_expensive_price = df['UnitPrice'].max()\n", + "least_expensive_price = df['UnitPrice'].min()\n", + "print('Most expensive item price:', most_expensive_price)\n", + "print('Least expensive item price:', least_expensive_price)\n", + "\n", + "price_difference = most_expensive_price - least_expensive_price\n", + "print('Price difference:', price_difference)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -253,7 +453,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.10.7" } }, "nbformat": 4,