diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index a6c64554..dd64017e 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -44,11 +44,34 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "series = pd.Series(lst)\n", + "series" ] }, { @@ -62,11 +85,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "74.4\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "print(series[2])\n" ] }, { @@ -78,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -96,11 +128,145 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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01234
053.195.067.535.078.4
161.340.830.837.887.6
220.673.244.214.691.8
357.40.196.14.269.5
483.620.585.422.835.9
549.069.00.131.889.1
623.340.795.083.826.9
727.626.453.888.868.5
896.696.453.472.450.1
973.739.043.281.634.7
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score_1score_2score_3score_4score_5
053.195.067.535.078.4
161.340.830.837.887.6
220.673.244.214.691.8
357.40.196.14.269.5
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727.626.453.888.868.5
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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": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "b_names = [\"score_1\", \"score_2\", \"score_3\", \"score_4\", \"score_5\"]\n", + "df.columns = b_names\n", + "df\n" ] }, { @@ -146,11 +447,123 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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score_1score_3score_5
053.167.578.4
161.330.887.6
220.644.291.8
357.496.169.5
483.685.435.9
549.00.189.1
623.395.026.9
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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": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "df1 = df.loc[:, [\"score_1\", \"score_3\", \"score_5\"]]\n", + "df1" ] }, { @@ -162,11 +575,21 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "56.95000000000001\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "score3_mean = df[\"score_3\"].mean()\n", + "print(score3_mean)" ] }, { @@ -178,11 +601,21 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "88.8\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "score4_max = df[\"score_4\"].max()\n", + "print(score4_max)" ] }, { @@ -194,11 +627,34 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 0.1\n", + "4 20.5\n", + "7 26.4\n", + "9 39.0\n", + "6 40.7\n", + "1 40.8\n", + "5 69.0\n", + "2 73.2\n", + "0 95.0\n", + "8 96.4\n", + "Name: score_2, dtype: float64\n", + "40.75\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "sorted_score2 = df[\"score_2\"].sort_values(ascending=True)\n", + "print(sorted_score2)\n", + "score2_median = df[\"score_2\"].median()\n", + "print(score2_median)" ] }, { @@ -210,9 +666,130 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [], + "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
\n", + "
" + ], + "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": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "orders = {'Description': ['LUNCH BAG APPLE DESIGN',\n", " 'SET OF 60 VINTAGE LEAF CAKE CASES ',\n", @@ -226,16 +803,140 @@ " '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]}" + " 'Revenue': [1.65, 13.2, 1.65, 518.4, 2.5, 5.95, 11.95, 19.8, 1.9, 60.0]}\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, - "outputs": [], + "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
\n", + "
" + ], + "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": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "df_orders = pd.DataFrame(orders)\n", + "df_orders" ] }, { @@ -247,11 +948,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total quantity ordered = 2978\n", + "revenue = 637.0\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "tqo = df_orders[\"Quantity\"].sum()\n", + "print(\"total quantity ordered = \", tqo)\n", + "rev = df_orders[\"Revenue\"].sum()\n", + "print(\"revenue = \", rev)" ] }, { @@ -263,11 +977,27 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.95\n", + "0.18\n", + "11.77\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "max_price = df_orders[\"UnitPrice\"].max()\n", + "print(max_price)\n", + "min_price = df_orders[\"UnitPrice\"].min()\n", + "print(min_price)\n", + "diff = max_price - min_price\n", + "print(diff)" ] }, { @@ -279,12 +1009,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "# Run this code:\n", - "admissions = pd.read_csv('../Admission_Predict.csv')" + "admissions = pd.read_csv(r'C:\\Users\\traik\\Desktop\\ML_Bootcamp\\Week2\\Day2\\lab-pandas-en\\Admission_Predict.csv')" ] }, { @@ -296,11 +1026,130 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 36, "metadata": {}, - "outputs": [], + "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
\n", + "
" + ], + "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": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "admissions.head()" ] }, { @@ -312,11 +1161,59 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR \\\n", + "0 False False False False False False \n", + "1 False False False False False False \n", + "2 False False False False False False \n", + "3 False False False False False False \n", + "4 False False False False False False \n", + ".. ... ... ... ... ... ... \n", + "380 False False False False False False \n", + "381 False False False False False False \n", + "382 False False False False False False \n", + "383 False False False False False False \n", + "384 False False False False False False \n", + "\n", + " CGPA Research Chance of Admit \n", + "0 False False False \n", + "1 False False False \n", + "2 False False False \n", + "3 False False False \n", + "4 False False False \n", + ".. ... ... ... \n", + "380 False False False \n", + "381 False False False \n", + "382 False False False \n", + "383 False False False \n", + "384 False False False \n", + "\n", + "[385 rows x 9 columns]\n", + "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" + "# your code here\n", + "missing_data = pd.isnull(admissions)\n", + "print(missing_data)\n", + "missing_counts = missing_data.sum()\n", + "print(missing_counts)" ] }, { @@ -328,11 +1225,233 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
Serial No.
1133711844.54.59.6510.92
2231610433.03.58.0010.72
3332211033.52.58.6710.80
4431410322.03.08.2100.65
5533011554.53.09.3410.90
..............................
38138132411033.53.59.0410.82
38238232510733.03.59.1110.84
38338333011645.04.59.4510.91
38438431210333.54.08.7800.67
38538533311745.04.09.6610.95
\n", + "

385 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR \\\n", + "Serial No. \n", + "1 1 337 118 4 4.5 4.5 \n", + "2 2 316 104 3 3.0 3.5 \n", + "3 3 322 110 3 3.5 2.5 \n", + "4 4 314 103 2 2.0 3.0 \n", + "5 5 330 115 5 4.5 3.0 \n", + "... ... ... ... ... ... ... \n", + "381 381 324 110 3 3.5 3.5 \n", + "382 382 325 107 3 3.0 3.5 \n", + "383 383 330 116 4 5.0 4.5 \n", + "384 384 312 103 3 3.5 4.0 \n", + "385 385 333 117 4 5.0 4.0 \n", + "\n", + " CGPA Research Chance of Admit \n", + "Serial No. \n", + "1 9.65 1 0.92 \n", + "2 8.00 1 0.72 \n", + "3 8.67 1 0.80 \n", + "4 8.21 0 0.65 \n", + "5 9.34 1 0.90 \n", + "... ... ... ... \n", + "381 9.04 1 0.82 \n", + "382 9.11 1 0.84 \n", + "383 9.45 1 0.91 \n", + "384 8.78 0 0.67 \n", + "385 9.66 1 0.95 \n", + "\n", + "[385 rows x 9 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "# your code here\n", + "new_index = admissions[\"Serial No.\"]\n", + "admissions.index = new_index\n", + "admissions" ] }, { @@ -351,13 +1470,27 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 54, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "101\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "condition_1 = admissions[\"CGPA\"] > 9\n", + "condition_2 = admissions[\"Research\"] == 1\n", + "filitered_admissions = admissions[condition_1 & condition_2]\n", + "filitered_admissions\n", + "print(len(filitered_admissions.index))\n", + "\n" ] }, { @@ -369,17 +1502,37 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mean for chance of admission = 0.8019999999999999\n" + ] + } + ], "source": [ - "# your code here" + "# your code here\n", + "condition_3 = admissions[\"SOP\"] < 3.5\n", + "filitered_admissions_2 = admissions[condition_1 & condition_3]\n", + "filitered_admissions_2\n", + "chance_mean = filitered_admissions_2[\"Chance of Admit \"].mean()\n", + "print(\"mean for chance of admission = \", chance_mean)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -393,7 +1546,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.11.3" }, "toc": { "base_numbering": "",