diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index a6c64554..125fa9b0 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -44,11 +44,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "new_series = pd.Series(lst)" ] }, { @@ -62,11 +63,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(74.4)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "new_series[2]" ] }, { @@ -78,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -96,11 +109,31 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "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\n", + "new_dataframe = pd.DataFrame(b)\n", + "print(new_dataframe)" ] }, { @@ -112,29 +145,40 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 24, "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]]" + "list_names = [\"Score_1\", \"Score_2\", \"Score_3\", \"Score_4\", \"Score_5\"]" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 31, + "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": [ + "# your code here\n", + "new_dataframe.columns = list_names\n", + "print(new_dataframe)" ] }, { @@ -146,11 +190,31 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 43, + "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": [ + "# your code here\n", + "small_df = new_dataframe[[\"Score_1\",\"Score_3\", \"Score_5\"]]\n", + "print(small_df)" ] }, { @@ -162,11 +226,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.95000000000001\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "av_sc3 = new_dataframe[\"Score_3\"].mean()\n", + "print(av_sc3)" ] }, { @@ -178,11 +252,21 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88.8\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "mx_sc4 = new_dataframe[\"Score_4\"].max()\n", + "print(mx_sc4)\n" ] }, { @@ -194,11 +278,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.75\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "as_sc2 = new_dataframe[\"Score_2\"].median()\n", + "print(as_sc2)" ] }, { @@ -210,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -231,11 +325,31 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 65, + "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": [ + "# your code here\n", + "df_dict = pd.DataFrame(orders)\n", + "print(df_dict)\n" ] }, { @@ -247,11 +361,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "Total_ord = df_dict[\"Quantity\"].sum()\n", + "print(Total_ord)" ] }, { @@ -263,11 +387,22 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.77\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "me_prod = df_dict[\"UnitPrice\"].max()\n", + "le_prod = df_dict[\"UnitPrice\"].min()\n", + "print(me_prod- le_prod)" ] }, { @@ -279,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -296,11 +431,130 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1231610433.03.58.0010.72
2332211033.52.58.6710.80
3431410322.03.08.2100.65
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" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "0 1 337 118 4 4.5 4.5 9.65 \n", + "1 2 316 104 3 3.0 3.5 8.00 \n", + "2 3 322 110 3 3.5 2.5 8.67 \n", + "3 4 314 103 2 2.0 3.0 8.21 \n", + "4 5 330 115 5 4.5 3.0 9.34 \n", + "\n", + " Research Chance of Admit \n", + "0 1 0.92 \n", + "1 1 0.72 \n", + "2 1 0.80 \n", + "3 0 0.65 \n", + "4 1 0.90 " + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "admissions.head()" ] }, { @@ -312,11 +566,31 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serial No. 0\n", + "GRE Score 0\n", + "TOEFL Score 0\n", + "University Rating 0\n", + "SOP 0\n", + "LOR 0\n", + "CGPA 0\n", + "Research 0\n", + "Chance of Admit 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "# your code here\n", + "missing_val = pd.isnull(admissions)\n", + "miss_count = missing_val.sum()\n", + "print(miss_count)" ] }, { @@ -328,11 +602,21 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 105, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serial No.\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "admissions.set_index(\"Serial No.\", inplace=True)\n", + "print(admissions.index.name)" ] }, { @@ -351,13 +635,23 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 170, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "101\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "count_ad = ((admissions[\"CGPA\"] > 9) & (admissions[\"Research\"] == 1)).sum()\n", + "print(count_ad)\n" ] }, { @@ -369,17 +663,27 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 169, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "# your code here\n", + "count_sop = ((admissions[\"CGPA\"] > 9) & (admissions[\"SOP\"] < 3.5)).sum()\n", + "print(count_sop)\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -393,7 +697,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.13.7" }, "toc": { "base_numbering": "",