diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index a6c64554..b29cd368 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -44,11 +44,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "df = pd.Series(lst)" ] }, { @@ -62,11 +64,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(74.4)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "df[2]" ] }, { @@ -78,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -96,11 +110,32 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "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", + "\n", + "df_b = pd.DataFrame(b)\n", + "print(df_b)" ] }, { @@ -112,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -130,11 +165,32 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "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", + "df_b.columns = ['Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5']\n", + "df_b = df_b.rename(columns={0: 'Score_1', 1: 'Score_2', 2: 'Score_3', 3: 'Score_4', 4: 'Score_5'})\n", + "print(df_b)" ] }, { @@ -146,11 +202,31 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "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", + "subset = df_b[['Score_1', 'Score_3', 'Score_5']]\n", + "print(subset)" ] }, { @@ -162,11 +238,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.95000000000001\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "average_Score_3 = df_b['Score_3'].mean()\n", + "\n", + "print(average_Score_3)" ] }, { @@ -178,11 +266,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88.8\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "max_Score_4 = df_b['Score_4'].max()\n", + "print(max_Score_4)" ] }, { @@ -194,11 +293,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "40.75\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "median_Score_2 = df_b['Score_2'].median()\n", + "print(median_Score_2)" ] }, { @@ -210,7 +319,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -231,11 +340,31 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "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_orders = pd.DataFrame(orders)\n", + "print(df_orders)" ] }, { @@ -247,11 +376,26 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978\n", + "637.0\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "total_qtd = df_orders['Quantity'].sum()\n", + "total_rvn = df_orders['Revenue'].sum()\n", + "\n", + "print(total_qtd)\n", + "print(total_rvn)" ] }, { @@ -263,11 +407,29 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.95\n", + "0.18\n", + "11.77\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "max_order = df_orders['UnitPrice'].max()\n", + "min_order = df_orders['UnitPrice'].min()\n", + "\n", + "print(max_order)\n", + "print(min_order)\n", + "\n", + "print(max_order - min_order)" ] }, { @@ -279,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -296,11 +458,131 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 32, + "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
4533011554.53.09.3410.90
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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": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "admissions.head()" ] }, { @@ -312,11 +594,33 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "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" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "\n", + "admissions.isnull().sum()" ] }, { @@ -328,11 +632,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "\n", + "admissions.set_index('Serial No.', inplace=True)" ] }, { @@ -351,13 +657,25 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [], - "source": [ - "# your code here" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of rows: 101\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "filtered_rows = admissions[(admissions['CGPA'] > 9) & (admissions['Research'] == 1)]\n", + "number_of_rows = len(filtered_rows)\n", + "print(f\"Number of rows: {number_of_rows}\")" ] }, { @@ -369,17 +687,29 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here" + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean chance of admit: 0.8019999999999999\n" + ] + } + ], + "source": [ + "# your code here\n", + "\n", + "filtered_applicants = admissions[(admissions['CGPA'] > 9) & (admissions['SOP'] < 3.5)]\n", + "mean_chance = filtered_applicants['Chance of Admit '].mean() # ← note o espaço no final\n", + "print(f\"Mean chance of admit: {mean_chance}\")" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "base", "language": "python", "name": "python3" }, @@ -393,7 +723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.13.5" }, "toc": { "base_numbering": "",