diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..4b2cd1e 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": 218, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 219, "metadata": {}, "outputs": [], "source": [ @@ -44,10 +44,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 220, "metadata": {}, - "outputs": [], - "source": [] + "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": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_1 = pd.Series(lst)\n", + "series_1" + ] }, { "cell_type": "markdown", @@ -60,10 +84,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 221, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 221, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_1[2]" + ] }, { "cell_type": "markdown", @@ -74,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 222, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +129,145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 223, "metadata": {}, - "outputs": [], - "source": [] + "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
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" + ], + "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": 231, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "products = pd.DataFrame(orders)\n", + "products" + ] }, { "cell_type": "markdown", @@ -238,10 +809,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 232, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2978\n" + ] + }, + { + "data": { + "text/plain": [ + "637.0" + ] + }, + "execution_count": 232, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_quantity = products.Quantity.sum()\n", + "print(total_quantity)\n", + "\n", + "total_revenue = products.Revenue.sum()\n", + "total_revenue\n" + ] }, { "cell_type": "markdown", @@ -252,10 +847,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 233, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11.95\n" + ] + }, + { + "data": { + "text/plain": [ + "0.18" + ] + }, + "execution_count": 233, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "most_expensive = products.UnitPrice.max()\n", + "print(most_expensive)\n", + "\n", + "least_expensive = products.UnitPrice.min()\n", + "least_expensive" + ] }, { "cell_type": "markdown", @@ -266,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -285,10 +904,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 235, "metadata": {}, - "outputs": [], - "source": [] + "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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GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
Serial No.
133711844.54.59.6510.92
231610433.03.58.0010.72
332211033.52.58.6710.80
431410322.03.08.2100.65
533011554.53.09.3410.90
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385 rows × 8 columns

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" + ], + "text/plain": [ + " GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "Serial No. \n", + "1 337 118 4 4.5 4.5 9.65 \n", + "2 316 104 3 3.0 3.5 8.00 \n", + "3 322 110 3 3.5 2.5 8.67 \n", + "4 314 103 2 2.0 3.0 8.21 \n", + "5 330 115 5 4.5 3.0 9.34 \n", + "... ... ... ... ... ... ... \n", + "381 324 110 3 3.5 3.5 9.04 \n", + "382 325 107 3 3.0 3.5 9.11 \n", + "383 330 116 4 5.0 4.5 9.45 \n", + "384 312 103 3 3.5 4.0 8.78 \n", + "385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit \n", + "Serial No. \n", + "1 1 0.92 \n", + "2 1 0.72 \n", + "3 1 0.80 \n", + "4 0 0.65 \n", + "5 1 0.90 \n", + "... ... ... \n", + "381 1 0.82 \n", + "382 1 0.84 \n", + "383 1 0.91 \n", + "384 0 0.67 \n", + "385 1 0.95 \n", + "\n", + "[385 rows x 8 columns]" + ] + }, + "execution_count": 237, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.set_index(\"Serial No.\")\n" + ] }, { "cell_type": "code", @@ -334,10 +1303,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 293, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 293, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.duplicated(subset=[\"GRE Score\", \"CGPA\"]).sum()" + ] }, { "cell_type": "markdown", @@ -348,10 +1330,220 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 239, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
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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", + "4 5 330 115 5 4.5 3.0 9.34 \n", + "10 11 328 112 4 4.0 4.5 9.10 \n", + "19 20 328 116 5 5.0 5.0 9.50 \n", + "20 21 334 119 5 5.0 4.5 9.70 \n", + ".. ... ... ... ... ... ... ... \n", + "379 380 329 111 4 4.5 4.0 9.23 \n", + "380 381 324 110 3 3.5 3.5 9.04 \n", + "381 382 325 107 3 3.0 3.5 9.11 \n", + "382 383 330 116 4 5.0 4.5 9.45 \n", + "384 385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit \n", + "0 1 0.92 \n", + "4 1 0.90 \n", + "10 1 0.78 \n", + "19 1 0.94 \n", + "20 1 0.95 \n", + ".. ... ... \n", + "379 1 0.89 \n", + "380 1 0.82 \n", + "381 1 0.84 \n", + "382 1 0.91 \n", + "384 1 0.95 \n", + "\n", + "[101 rows x 9 columns]" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "condition_1 = admissions[\"CGPA\"] > 9\n", + "condition_2 = admissions[\"Research\"] != 0\n", + "\n", + "admissions[condition_1 & condition_2]" + ] }, { "cell_type": "markdown", @@ -362,17 +1554,244 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 240, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8019999999999999\n" + ] + } + ], + "source": [ + "condition_3 = admissions[\"CGPA\"] > 9\n", + "condition_4 = admissions[\"SOP\"] < 3.5\n", + "\n", + "print(admissions[condition_3 & condition_4][\"Chance of Admit\"].mean())\n", + "\n", + "\n", + "filtered_admisions = admissions[condition_3 & condition_4]\n", + "\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 241, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
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385 rows × 9 columns

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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", + "380 381 324 110 3 3.5 3.5 9.04 \n", + "381 382 325 107 3 3.0 3.5 9.11 \n", + "382 383 330 116 4 5.0 4.5 9.45 \n", + "383 384 312 103 3 3.5 4.0 8.78 \n", + "384 385 333 117 4 5.0 4.0 9.66 \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 \n", + ".. ... ... \n", + "380 1 0.82 \n", + "381 1 0.84 \n", + "382 1 0.91 \n", + "383 0 0.67 \n", + "384 1 0.95 \n", + "\n", + "[385 rows x 9 columns]" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#same as before different process\n", + "\n", + "filtered_admisions[\"Chance of Admit\"].mean()\n", + "admissions" + ] }, { "cell_type": "markdown", @@ -384,10 +1803,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 244, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def toefl_grades (i):\n", + " if i > 100:\n", + " return True\n", + " else:\n", + " return False\n", + " " + ] }, { "cell_type": "markdown", @@ -398,31 +1824,578 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 282, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitDecision
373831610522.52.58.210.49True
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" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "37 38 316 105 2 2.5 2.5 8.2 \n", + "\n", + " Research Chance of Admit Decision \n", + "37 1 0.49 True " + ] + }, + "execution_count": 282, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions[\"Decision\"] = admissions[\"TOEFL Score\"].apply(toefl_grades)\n", + "admissions.sample()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a column called `decision2` in the `admissions` dataframe. Assign 1 to this column if the value of `SOP` is greater than 3 and 0 otherwise. \n", + "HINT (use np.where)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 286, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def sop_grades (i):\n", + " if i > 3:\n", + " return 1\n", + " else:\n", + " return 0\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 287, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitDecisiondecision2
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385 rows × 11 columns

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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", + "380 381 324 110 3 3.5 3.5 9.04 \n", + "381 382 325 107 3 3.0 3.5 9.11 \n", + "382 383 330 116 4 5.0 4.5 9.45 \n", + "383 384 312 103 3 3.5 4.0 8.78 \n", + "384 385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit Decision decision2 \n", + "0 1 0.92 True 1 \n", + "1 1 0.72 True 0 \n", + "2 1 0.80 True 1 \n", + "3 0 0.65 True 0 \n", + "4 1 0.90 True 1 \n", + ".. ... ... ... ... \n", + "380 1 0.82 True 1 \n", + "381 1 0.84 True 0 \n", + "382 1 0.91 True 1 \n", + "383 0 0.67 True 1 \n", + "384 1 0.95 True 1 \n", + "\n", + "[385 rows x 11 columns]" + ] + }, + "execution_count": 287, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions[\"decision2\"] = admissions[\"SOP\"].apply(sop_grades)\n", + "admissions" + ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 295, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitDecisiondecision2
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2332211033.52.58.6710.80True1
3431410322.03.08.2100.65True0
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38038132411033.53.59.0410.82True1
38138232510733.03.59.1110.84True0
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385 rows × 11 columns

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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", + "380 381 324 110 3 3.5 3.5 9.04 \n", + "381 382 325 107 3 3.0 3.5 9.11 \n", + "382 383 330 116 4 5.0 4.5 9.45 \n", + "383 384 312 103 3 3.5 4.0 8.78 \n", + "384 385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit Decision decision2 \n", + "0 1 0.92 True 1 \n", + "1 1 0.72 True 0 \n", + "2 1 0.80 True 1 \n", + "3 0 0.65 True 0 \n", + "4 1 0.90 True 1 \n", + ".. ... ... ... ... \n", + "380 1 0.82 True 1 \n", + "381 1 0.84 True 0 \n", + "382 1 0.91 True 1 \n", + "383 0 0.67 True 1 \n", + "384 1 0.95 True 1 \n", + "\n", + "[385 rows x 11 columns]" + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Create a column called `decision2` in the `admissions` dataframe. Assign 1 to this column if the value of `SOP` is greater than 3 and 0 otherwise. \n", - "HINT (use np.where)" + "# USING WHERE\n", + "\n", + "condition_5 = admissions[\"SOP\"] > 3\n", + "admissions[\"decision2\"] = np.where( condition_5, 1, 0)\n", + "admissions\n" ] }, { @@ -449,7 +2422,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "toc": { "base_numbering": "",