diff --git a/your-code/main.ipynb b/your-code/main.ipynb index f50ae3d..8b8d9ff 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -18,11 +18,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "import numpy as np\n", + "import pandas as pd \n" ] }, { @@ -34,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,11 +44,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "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": [ - "# your code here" + "panda_series = pd.Series(lst) \n", + "\n", + "print(panda_series)" ] }, { @@ -61,11 +82,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "panda_series[2]" ] }, { @@ -77,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -95,11 +127,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "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": [ - "# your code here" + "data_frame = pd.DataFrame(np.array([[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]]))\n", + "print(data_frame)" ] }, { @@ -111,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -120,11 +180,145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_frame.columns = (colnames) \n", + "\n", + "data_frame" ] }, { @@ -136,11 +330,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Score_1 Score_2 Score_3\n", + "0 53.1 95.0 67.5\n", + "1 61.3 40.8 30.8\n", + "2 20.6 73.2 44.2\n", + "3 57.4 0.1 96.1\n", + "4 83.6 20.5 85.4\n", + "5 49.0 69.0 0.1\n", + "6 23.3 40.7 95.0\n", + "7 27.6 26.4 53.8\n", + "8 96.6 96.4 53.4\n", + "9 73.7 39.0 43.2\n" + ] + } + ], "source": [ - "# your code here" + "data_subset = data_frame[['Score_1', 'Score_2', 'Score_3']]\n", + "print(data_subset)" ] }, { @@ -152,11 +365,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "56.95000000000001" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_frame ['Score_3'].mean()" ] }, { @@ -168,11 +392,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "88.8" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_frame ['Score_4'].max()" ] }, { @@ -184,11 +419,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "40.75" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_frame ['Score_2'].median()" ] }, { @@ -200,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -221,11 +467,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "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": [ - "# your code here" + "data_frame_2 = pd.DataFrame(orders)\n", + "print(data_frame_2)" ] }, { @@ -237,11 +502,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Total quantity of orders 2978\n", + " Total quantity of revenue generated 637.0\n" + ] + } + ], "source": [ - "# your code here" + "print(f\" Total quantity of orders {data_frame_2 ['Quantity'].sum()}\")\n", + "print(f\" Total quantity of revenue generated {data_frame_2 ['Revenue'].sum()}\")" ] }, { @@ -253,12 +528,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.18" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data_frame_2 ['UnitPrice'].min()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -277,7 +570,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.9.1" } }, "nbformat": 4,