diff --git a/main.ipynb b/main.ipynb new file mode 100644 index 0000000..22b9cd8 --- /dev/null +++ b/main.ipynb @@ -0,0 +1,5384 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Before your start:\n", + "- Read the README.md file\n", + "- Comment as much as you can and use the resources (README.md file)\n", + "- Happy learning!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import numpy and pandas\n", + "import numpy as np\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1 - Index Functions\n", + "\n", + "In this challenge, we will practice the advanced functions introduced in the lesson. However, before we start, we will load and evaluate our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "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
5632110933.04.08.2010.75
6730810123.04.07.9000.68
7830210212.01.58.0000.50
8932310833.53.08.6000.45
91032711144.04.59.0010.84
101132811244.04.59.1010.78
111230710934.03.08.0010.62
121331110433.52.08.2010.61
131431410533.52.58.3000.54
141531710734.03.08.7000.66
151631910634.03.08.0010.65
161731811034.03.08.8000.63
171830310233.53.08.5000.62
181932511443.02.08.4000.70
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202133411955.04.59.7010.95
212233611954.03.59.8010.97
222332210954.53.58.8000.76
23242989821.52.57.5010.44
24252959312.02.07.2000.46
25263109921.52.07.3000.54
26273009723.03.08.1010.65
272832710334.04.08.3010.74
282933811843.04.59.4010.91
293034011454.04.09.6010.90
..............................
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36536632210433.54.08.8410.78
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3733742969721.52.07.8000.49
37437532010833.54.08.4410.76
37537631410222.02.58.2400.64
37637731810632.03.08.6500.71
37737832611244.03.59.1210.84
37837931710423.03.08.7600.77
37938032911144.54.09.2310.89
38038132411033.53.59.0410.82
38138232510733.03.59.1110.84
38238333011645.04.59.4510.91
38338431210333.54.08.7800.67
38438533311745.04.09.6610.95
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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", + "5 6 321 109 3 3.0 4.0 8.20 \n", + "6 7 308 101 2 3.0 4.0 7.90 \n", + "7 8 302 102 1 2.0 1.5 8.00 \n", + "8 9 323 108 3 3.5 3.0 8.60 \n", + "9 10 327 111 4 4.0 4.5 9.00 \n", + "10 11 328 112 4 4.0 4.5 9.10 \n", + "11 12 307 109 3 4.0 3.0 8.00 \n", + "12 13 311 104 3 3.5 2.0 8.20 \n", + "13 14 314 105 3 3.5 2.5 8.30 \n", + "14 15 317 107 3 4.0 3.0 8.70 \n", + "15 16 319 106 3 4.0 3.0 8.00 \n", + "16 17 318 110 3 4.0 3.0 8.80 \n", + "17 18 303 102 3 3.5 3.0 8.50 \n", + "18 19 325 114 4 3.0 2.0 8.40 \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", + "21 22 336 119 5 4.0 3.5 9.80 \n", + "22 23 322 109 5 4.5 3.5 8.80 \n", + "23 24 298 98 2 1.5 2.5 7.50 \n", + "24 25 295 93 1 2.0 2.0 7.20 \n", + "25 26 310 99 2 1.5 2.0 7.30 \n", + "26 27 300 97 2 3.0 3.0 8.10 \n", + "27 28 327 103 3 4.0 4.0 8.30 \n", + "28 29 338 118 4 3.0 4.5 9.40 \n", + "29 30 340 114 5 4.0 4.0 9.60 \n", + ".. ... ... ... ... ... ... ... \n", + "355 356 310 103 2 2.5 2.5 8.24 \n", + "356 357 324 110 3 3.5 3.0 9.22 \n", + "357 358 336 119 4 4.5 4.0 9.62 \n", + "358 359 321 109 3 3.0 3.0 8.54 \n", + "359 360 315 105 2 2.0 2.5 7.65 \n", + "360 361 304 101 2 2.0 2.5 7.66 \n", + "361 362 297 96 2 2.5 2.0 7.43 \n", + "362 363 290 100 1 1.5 2.0 7.56 \n", + "363 364 303 98 1 2.0 2.5 7.65 \n", + "364 365 311 99 1 2.5 3.0 8.43 \n", + "365 366 322 104 3 3.5 4.0 8.84 \n", + "366 367 319 105 3 3.0 3.5 8.67 \n", + "367 368 324 110 4 4.5 4.0 9.15 \n", + "368 369 300 100 3 3.0 3.5 8.26 \n", + "369 370 340 113 4 5.0 5.0 9.74 \n", + "370 371 335 117 5 5.0 5.0 9.82 \n", + "371 372 302 101 2 2.5 3.5 7.96 \n", + "372 373 307 105 2 2.0 3.5 8.10 \n", + "373 374 296 97 2 1.5 2.0 7.80 \n", + "374 375 320 108 3 3.5 4.0 8.44 \n", + "375 376 314 102 2 2.0 2.5 8.24 \n", + "376 377 318 106 3 2.0 3.0 8.65 \n", + "377 378 326 112 4 4.0 3.5 9.12 \n", + "378 379 317 104 2 3.0 3.0 8.76 \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", + "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", + "5 1 0.75 \n", + "6 0 0.68 \n", + "7 0 0.50 \n", + "8 0 0.45 \n", + "9 1 0.84 \n", + "10 1 0.78 \n", + "11 1 0.62 \n", + "12 1 0.61 \n", + "13 0 0.54 \n", + "14 0 0.66 \n", + "15 1 0.65 \n", + "16 0 0.63 \n", + "17 0 0.62 \n", + "18 0 0.70 \n", + "19 1 0.94 \n", + "20 1 0.95 \n", + "21 1 0.97 \n", + "22 0 0.76 \n", + "23 1 0.44 \n", + "24 0 0.46 \n", + "25 0 0.54 \n", + "26 1 0.65 \n", + "27 1 0.74 \n", + "28 1 0.91 \n", + "29 1 0.90 \n", + ".. ... ... \n", + "355 0 0.72 \n", + "356 1 0.89 \n", + "357 1 0.95 \n", + "358 1 0.79 \n", + "359 0 0.39 \n", + "360 0 0.38 \n", + "361 0 0.34 \n", + "362 0 0.47 \n", + "363 0 0.56 \n", + "364 1 0.71 \n", + "365 1 0.78 \n", + "366 1 0.73 \n", + "367 1 0.82 \n", + "368 0 0.62 \n", + "369 1 0.96 \n", + "370 1 0.96 \n", + "371 0 0.46 \n", + "372 0 0.53 \n", + "373 0 0.49 \n", + "374 1 0.76 \n", + "375 0 0.64 \n", + "376 0 0.71 \n", + "377 1 0.84 \n", + "378 0 0.77 \n", + "379 1 0.89 \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": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run this code:\n", + "\n", + "admissions = pd.read_csv('../Admission_Predict.csv')\n", + "admissions\n", + "#to remove trailing spaces at the end of the column names\n", + "admissions.columns = admissions.columns.str.rstrip() #cosa de este lab, no visto en otros\n", + "admissions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate the dataset by looking at the `head` function." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admissions.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before beginning to work with this dataset and evaluating graduate admissions data, we will verify that there is no missing data in the dataset. Do this in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admissions.isnull().sum()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interestingly, there is a column that uniquely identifies the applicants. This column is the serial number column. Instead of having our own index, we should make this column our index. Do this in the cell below. Keep the column in the dataframe in addition to making it an index." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "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
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" + ], + "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", + " 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 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admissions.index = admissions['Serial No.']\n", + "admissions.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Turns out that `GRE Score` and `CGPA` also uniquely identify the data. Show this in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
GRE Score
29011329010442.02.57.4600.45
29036329010011.52.07.5600.47
2931622939722.04.07.8010.64
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2973162979622.51.57.8900.43
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2993329910624.04.08.4000.64
29911229910234.03.58.6200.56
29917629910023.03.57.8800.68
29922829910011.52.07.8900.59
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385 rows × 9 columns

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" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR \\\n", + "GRE Score \n", + "290 113 290 104 4 2.0 2.5 \n", + "290 363 290 100 1 1.5 2.0 \n", + "293 162 293 97 2 2.0 4.0 \n", + "294 260 294 95 1 1.5 1.5 \n", + "294 76 294 93 1 1.5 2.0 \n", + "295 25 295 93 1 2.0 2.0 \n", + "295 330 295 96 2 1.5 2.0 \n", + "295 314 295 101 2 2.5 2.0 \n", + "295 196 295 99 2 2.5 3.0 \n", + "296 374 296 97 2 1.5 2.0 \n", + "296 215 296 99 2 2.5 2.5 \n", + "296 75 296 95 2 3.0 2.0 \n", + "296 114 296 99 2 3.0 3.5 \n", + "296 229 296 101 1 2.5 3.0 \n", + "297 340 297 98 2 2.5 3.0 \n", + "297 316 297 96 2 2.5 1.5 \n", + "297 362 297 96 2 2.5 2.0 \n", + "297 153 297 100 1 1.5 2.0 \n", + "298 88 298 98 2 4.0 3.0 \n", + "298 24 298 98 2 1.5 2.5 \n", + "298 54 298 99 2 4.0 2.0 \n", + "298 195 298 105 3 3.5 4.0 \n", + "298 303 298 101 2 1.5 2.0 \n", + "298 354 298 92 1 2.0 2.0 \n", + "298 155 298 99 1 1.5 3.0 \n", + "299 259 299 96 2 1.5 2.0 \n", + "299 33 299 106 2 4.0 4.0 \n", + "299 112 299 102 3 4.0 3.5 \n", + "299 176 299 100 2 3.0 3.5 \n", + "299 228 299 100 1 1.5 2.0 \n", + "... ... ... ... ... ... ... \n", + "334 144 334 114 4 4.0 4.0 \n", + "334 165 334 117 5 4.0 4.5 \n", + "334 49 334 116 4 4.0 3.0 \n", + "334 194 334 120 5 4.0 5.0 \n", + "334 21 334 119 5 5.0 4.5 \n", + "334 117 334 119 5 4.5 4.5 \n", + "335 371 335 117 5 5.0 5.0 \n", + "335 242 335 115 4 4.5 4.5 \n", + "335 180 335 118 5 4.5 3.5 \n", + "335 116 335 117 5 5.0 5.0 \n", + "336 274 336 118 5 4.5 4.0 \n", + "336 68 336 112 5 5.0 5.0 \n", + "336 22 336 119 5 4.0 3.5 \n", + "336 358 336 119 4 4.5 4.0 \n", + "336 185 336 118 5 4.5 5.0 \n", + "337 1 337 118 4 4.5 4.5 \n", + "338 348 338 115 5 4.5 5.0 \n", + "338 203 338 120 4 5.0 5.0 \n", + "338 103 338 117 4 3.5 4.5 \n", + "338 29 338 118 4 3.0 4.5 \n", + "339 142 339 116 4 4.0 3.5 \n", + "339 126 339 114 5 4.0 4.5 \n", + "339 44 339 119 5 4.5 4.0 \n", + "340 272 340 112 4 5.0 4.5 \n", + "340 137 340 120 4 4.5 4.0 \n", + "340 81 340 115 5 4.5 4.5 \n", + "340 78 340 120 4 5.0 5.0 \n", + "340 30 340 114 5 4.0 4.0 \n", + "340 370 340 113 4 5.0 5.0 \n", + "340 193 340 120 5 4.5 4.5 \n", + "\n", + " CGPA Research Chance of Admit \n", + "GRE Score \n", + "290 7.46 0 0.45 \n", + "290 7.56 0 0.47 \n", + "293 7.80 1 0.64 \n", + "294 7.64 0 0.49 \n", + "294 7.36 0 0.46 \n", + "295 7.20 0 0.46 \n", + "295 7.34 0 0.47 \n", + "295 7.86 0 0.69 \n", + "295 7.65 0 0.57 \n", + "296 7.80 0 0.49 \n", + "296 8.03 0 0.61 \n", + "296 7.54 1 0.44 \n", + "296 7.28 0 0.47 \n", + "296 7.68 0 0.60 \n", + "297 7.67 0 0.59 \n", + "297 7.89 0 0.43 \n", + "297 7.43 0 0.34 \n", + "297 7.90 0 0.52 \n", + "298 8.03 0 0.34 \n", + "298 7.50 1 0.44 \n", + "298 7.60 0 0.46 \n", + "298 8.54 0 0.69 \n", + "298 7.86 0 0.54 \n", + "298 7.88 0 0.51 \n", + "298 7.46 0 0.53 \n", + "299 7.86 0 0.54 \n", + "299 8.40 0 0.64 \n", + "299 8.62 0 0.56 \n", + "299 7.88 0 0.68 \n", + "299 7.89 0 0.59 \n", + "... ... ... ... \n", + "334 9.43 1 0.93 \n", + "334 9.07 1 0.89 \n", + "334 8.00 1 0.78 \n", + "334 9.87 1 0.97 \n", + "334 9.70 1 0.95 \n", + "334 9.48 1 0.94 \n", + "335 9.82 1 0.96 \n", + "335 9.68 1 0.93 \n", + "335 9.44 1 0.93 \n", + "335 9.56 1 0.94 \n", + "336 9.19 1 0.92 \n", + "336 9.76 1 0.96 \n", + "336 9.80 1 0.97 \n", + "336 9.62 1 0.95 \n", + "336 9.53 1 0.94 \n", + "337 9.65 1 0.92 \n", + "338 9.23 1 0.91 \n", + "338 9.66 1 0.95 \n", + "338 9.46 1 0.91 \n", + "338 9.40 1 0.91 \n", + "339 9.80 1 0.96 \n", + "339 9.76 1 0.96 \n", + "339 9.70 0 0.89 \n", + "340 9.66 1 0.94 \n", + "340 9.92 1 0.97 \n", + "340 9.45 1 0.94 \n", + "340 9.50 1 0.96 \n", + "340 9.60 1 0.90 \n", + "340 9.74 1 0.96 \n", + "340 9.91 1 0.97 \n", + "\n", + "[385 rows x 9 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admissions.index = admissions['GRE Score']\n", + "admissions.sort_index()\n", + "#observamos que hay al menos dos estudiantes con el mismo GRE Score por lo que no es un índice único\n", + "#ejemplos 290 y 294" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
CGPA
6.80553009913.02.06.8010.36
7.20252959312.02.07.2000.46
7.253343029912.02.07.2500.57
7.281142969923.03.57.2800.47
7.30263109921.52.07.3000.54
7.343302959621.52.07.3400.47
7.343332999411.01.07.3400.42
7.36762949311.52.07.3600.46
7.405331610232.03.07.4000.64
7.433622979622.52.07.4300.34
7.433313169811.52.07.4300.49
7.461552989911.53.07.4600.53
7.4611329010442.02.57.4600.45
7.463533119811.02.57.4600.57
7.50242989821.52.57.5010.44
7.503530410513.01.57.5000.52
7.54752969523.02.07.5410.44
7.5636329010011.52.07.5600.47
7.60542989924.02.07.6000.46
7.642602949511.51.57.6400.49
7.6434431410522.52.07.6400.70
7.643323049721.52.07.6400.47
7.6527830710522.53.07.6500.58
7.653643039812.02.57.6500.56
7.651962959922.53.07.6500.57
7.6536031510522.02.57.6500.39
7.6636130410122.02.57.6600.38
7.66872999735.03.57.6600.38
7.66903039932.02.57.6600.36
7.673402979822.53.07.6700.59
..............................
9.4538333011645.04.59.4510.91
9.4610333811743.54.59.4610.91
9.4732333211855.05.09.4710.94
9.4811733411954.54.59.4810.94
9.502032811655.05.09.5010.94
9.507834012045.05.09.5010.96
9.5318533611854.55.09.5310.94
9.5434733411644.03.59.5410.93
9.5611633511755.05.09.5610.94
9.603034011454.04.09.6010.90
9.6235833611944.54.09.6210.95
9.646733211855.05.09.6410.94
9.65133711844.54.59.6510.92
9.6638533311745.04.09.6610.95
9.6620333812045.05.09.6610.95
9.6627234011245.04.59.6610.94
9.6824233511544.54.59.6810.93
9.704433911954.54.09.7000.89
9.702133411955.04.59.7010.95
9.7437034011345.05.09.7410.96
9.7612633911454.04.59.7610.96
9.766833611255.05.09.7610.96
9.7820433311955.04.59.7810.96
9.8014233911644.03.59.8010.96
9.803133111254.05.09.8010.94
9.802233611954.03.59.8010.97
9.8237133511755.05.09.8210.96
9.8719433412054.05.09.8710.97
9.9119334012054.54.59.9110.97
9.9213734012044.54.09.9210.97
\n", + "

385 rows × 9 columns

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" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "CGPA \n", + "6.80 55 300 99 1 3.0 2.0 6.80 \n", + "7.20 25 295 93 1 2.0 2.0 7.20 \n", + "7.25 334 302 99 1 2.0 2.0 7.25 \n", + "7.28 114 296 99 2 3.0 3.5 7.28 \n", + "7.30 26 310 99 2 1.5 2.0 7.30 \n", + "7.34 330 295 96 2 1.5 2.0 7.34 \n", + "7.34 333 299 94 1 1.0 1.0 7.34 \n", + "7.36 76 294 93 1 1.5 2.0 7.36 \n", + "7.40 53 316 102 3 2.0 3.0 7.40 \n", + "7.43 362 297 96 2 2.5 2.0 7.43 \n", + "7.43 331 316 98 1 1.5 2.0 7.43 \n", + "7.46 155 298 99 1 1.5 3.0 7.46 \n", + "7.46 113 290 104 4 2.0 2.5 7.46 \n", + "7.46 353 311 98 1 1.0 2.5 7.46 \n", + "7.50 24 298 98 2 1.5 2.5 7.50 \n", + "7.50 35 304 105 1 3.0 1.5 7.50 \n", + "7.54 75 296 95 2 3.0 2.0 7.54 \n", + "7.56 363 290 100 1 1.5 2.0 7.56 \n", + "7.60 54 298 99 2 4.0 2.0 7.60 \n", + "7.64 260 294 95 1 1.5 1.5 7.64 \n", + "7.64 344 314 105 2 2.5 2.0 7.64 \n", + "7.64 332 304 97 2 1.5 2.0 7.64 \n", + "7.65 278 307 105 2 2.5 3.0 7.65 \n", + "7.65 364 303 98 1 2.0 2.5 7.65 \n", + "7.65 196 295 99 2 2.5 3.0 7.65 \n", + "7.65 360 315 105 2 2.0 2.5 7.65 \n", + "7.66 361 304 101 2 2.0 2.5 7.66 \n", + "7.66 87 299 97 3 5.0 3.5 7.66 \n", + "7.66 90 303 99 3 2.0 2.5 7.66 \n", + "7.67 340 297 98 2 2.5 3.0 7.67 \n", + "... ... ... ... ... ... ... ... \n", + "9.45 383 330 116 4 5.0 4.5 9.45 \n", + "9.46 103 338 117 4 3.5 4.5 9.46 \n", + "9.47 323 332 118 5 5.0 5.0 9.47 \n", + "9.48 117 334 119 5 4.5 4.5 9.48 \n", + "9.50 20 328 116 5 5.0 5.0 9.50 \n", + "9.50 78 340 120 4 5.0 5.0 9.50 \n", + "9.53 185 336 118 5 4.5 5.0 9.53 \n", + "9.54 347 334 116 4 4.0 3.5 9.54 \n", + "9.56 116 335 117 5 5.0 5.0 9.56 \n", + "9.60 30 340 114 5 4.0 4.0 9.60 \n", + "9.62 358 336 119 4 4.5 4.0 9.62 \n", + "9.64 67 332 118 5 5.0 5.0 9.64 \n", + "9.65 1 337 118 4 4.5 4.5 9.65 \n", + "9.66 385 333 117 4 5.0 4.0 9.66 \n", + "9.66 203 338 120 4 5.0 5.0 9.66 \n", + "9.66 272 340 112 4 5.0 4.5 9.66 \n", + "9.68 242 335 115 4 4.5 4.5 9.68 \n", + "9.70 44 339 119 5 4.5 4.0 9.70 \n", + "9.70 21 334 119 5 5.0 4.5 9.70 \n", + "9.74 370 340 113 4 5.0 5.0 9.74 \n", + "9.76 126 339 114 5 4.0 4.5 9.76 \n", + "9.76 68 336 112 5 5.0 5.0 9.76 \n", + "9.78 204 333 119 5 5.0 4.5 9.78 \n", + "9.80 142 339 116 4 4.0 3.5 9.80 \n", + "9.80 31 331 112 5 4.0 5.0 9.80 \n", + "9.80 22 336 119 5 4.0 3.5 9.80 \n", + "9.82 371 335 117 5 5.0 5.0 9.82 \n", + "9.87 194 334 120 5 4.0 5.0 9.87 \n", + "9.91 193 340 120 5 4.5 4.5 9.91 \n", + "9.92 137 340 120 4 4.5 4.0 9.92 \n", + "\n", + " Research Chance of Admit \n", + "CGPA \n", + "6.80 1 0.36 \n", + "7.20 0 0.46 \n", + "7.25 0 0.57 \n", + "7.28 0 0.47 \n", + "7.30 0 0.54 \n", + "7.34 0 0.47 \n", + "7.34 0 0.42 \n", + "7.36 0 0.46 \n", + "7.40 0 0.64 \n", + "7.43 0 0.34 \n", + "7.43 0 0.49 \n", + "7.46 0 0.53 \n", + "7.46 0 0.45 \n", + "7.46 0 0.57 \n", + "7.50 1 0.44 \n", + "7.50 0 0.52 \n", + "7.54 1 0.44 \n", + "7.56 0 0.47 \n", + "7.60 0 0.46 \n", + "7.64 0 0.49 \n", + "7.64 0 0.70 \n", + "7.64 0 0.47 \n", + "7.65 0 0.58 \n", + "7.65 0 0.56 \n", + "7.65 0 0.57 \n", + "7.65 0 0.39 \n", + "7.66 0 0.38 \n", + "7.66 0 0.38 \n", + "7.66 0 0.36 \n", + "7.67 0 0.59 \n", + "... ... ... \n", + "9.45 1 0.91 \n", + "9.46 1 0.91 \n", + "9.47 1 0.94 \n", + "9.48 1 0.94 \n", + "9.50 1 0.94 \n", + "9.50 1 0.96 \n", + "9.53 1 0.94 \n", + "9.54 1 0.93 \n", + "9.56 1 0.94 \n", + "9.60 1 0.90 \n", + "9.62 1 0.95 \n", + "9.64 1 0.94 \n", + "9.65 1 0.92 \n", + "9.66 1 0.95 \n", + "9.66 1 0.95 \n", + "9.66 1 0.94 \n", + "9.68 1 0.93 \n", + "9.70 0 0.89 \n", + "9.70 1 0.95 \n", + "9.74 1 0.96 \n", + "9.76 1 0.96 \n", + "9.76 1 0.96 \n", + "9.78 1 0.96 \n", + "9.80 1 0.96 \n", + "9.80 1 0.94 \n", + "9.80 1 0.97 \n", + "9.82 1 0.96 \n", + "9.87 1 0.97 \n", + "9.91 1 0.97 \n", + "9.92 1 0.97 \n", + "\n", + "[385 rows x 9 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.index = admissions['CGPA']\n", + "admissions.sort_index()\n", + "#CGPA tampoco sirve como índice poruqe hay al menos dos personas con el mismo CGPA de 7.34" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the index with an index comprised of two columns - `GRE Score` and `CGPA`. Remove the columns from the dataset as well. Make sure to use `inplace=True`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now change the index back to a sequence starting at zero using the `reset_index` function. Make sure to use `inplace=True`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.TOEFL ScoreUniversity RatingSOPLORResearchChance of Admit
GRE ScoreCGPA
2907.4611310442.02.500.45
7.5636310011.52.000.47
2937.801629722.04.010.64
2947.36769311.52.000.46
7.642609511.51.500.49
2957.20259312.02.000.46
7.343309621.52.000.47
7.651969922.53.000.57
7.8631410122.52.000.69
2967.281149923.03.500.47
7.54759523.02.010.44
7.6822910112.53.000.60
7.803749721.52.000.49
8.032159922.52.500.61
2977.433629622.52.000.34
7.673409822.53.000.59
7.893169622.51.500.43
7.9015310011.52.000.52
2987.461559911.53.000.53
7.50249821.52.510.44
7.60549924.02.000.46
7.8630310121.52.000.54
7.883549212.02.000.51
8.03889824.03.000.34
8.5419510533.54.000.69
2997.343339411.01.000.42
7.66879735.03.500.38
7.862599621.52.000.54
7.8817610023.03.500.68
7.8922810011.52.000.59
...........................
3349.0716511754.04.510.89
9.4314411444.04.010.93
9.4811711954.54.510.94
9.5434711644.03.510.93
9.702111955.04.510.95
9.8719412054.05.010.97
3359.4418011854.53.510.93
9.5611611755.05.010.94
9.6824211544.54.510.93
9.8237111755.05.010.96
3369.1927411854.54.010.92
9.5318511854.55.010.94
9.6235811944.54.010.95
9.766811255.05.010.96
9.802211954.03.510.97
3379.65111844.54.510.92
3389.2334811554.55.010.91
9.402911843.04.510.91
9.4610311743.54.510.91
9.6620312045.05.010.95
3399.704411954.54.000.89
9.7612611454.04.510.96
9.8014211644.03.510.96
3409.458111554.54.510.94
9.507812045.05.010.96
9.603011454.04.010.90
9.6627211245.04.510.94
9.7437011345.05.010.96
9.9119312054.54.510.97
9.9213712044.54.010.97
\n", + "

385 rows × 7 columns

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" + ], + "text/plain": [ + " Serial No. TOEFL Score University Rating SOP LOR \\\n", + "GRE Score CGPA \n", + "290 7.46 113 104 4 2.0 2.5 \n", + " 7.56 363 100 1 1.5 2.0 \n", + "293 7.80 162 97 2 2.0 4.0 \n", + "294 7.36 76 93 1 1.5 2.0 \n", + " 7.64 260 95 1 1.5 1.5 \n", + "295 7.20 25 93 1 2.0 2.0 \n", + " 7.34 330 96 2 1.5 2.0 \n", + " 7.65 196 99 2 2.5 3.0 \n", + " 7.86 314 101 2 2.5 2.0 \n", + "296 7.28 114 99 2 3.0 3.5 \n", + " 7.54 75 95 2 3.0 2.0 \n", + " 7.68 229 101 1 2.5 3.0 \n", + " 7.80 374 97 2 1.5 2.0 \n", + " 8.03 215 99 2 2.5 2.5 \n", + "297 7.43 362 96 2 2.5 2.0 \n", + " 7.67 340 98 2 2.5 3.0 \n", + " 7.89 316 96 2 2.5 1.5 \n", + " 7.90 153 100 1 1.5 2.0 \n", + "298 7.46 155 99 1 1.5 3.0 \n", + " 7.50 24 98 2 1.5 2.5 \n", + " 7.60 54 99 2 4.0 2.0 \n", + " 7.86 303 101 2 1.5 2.0 \n", + " 7.88 354 92 1 2.0 2.0 \n", + " 8.03 88 98 2 4.0 3.0 \n", + " 8.54 195 105 3 3.5 4.0 \n", + "299 7.34 333 94 1 1.0 1.0 \n", + " 7.66 87 97 3 5.0 3.5 \n", + " 7.86 259 96 2 1.5 2.0 \n", + " 7.88 176 100 2 3.0 3.5 \n", + " 7.89 228 100 1 1.5 2.0 \n", + "... ... ... ... ... ... \n", + "334 9.07 165 117 5 4.0 4.5 \n", + " 9.43 144 114 4 4.0 4.0 \n", + " 9.48 117 119 5 4.5 4.5 \n", + " 9.54 347 116 4 4.0 3.5 \n", + " 9.70 21 119 5 5.0 4.5 \n", + " 9.87 194 120 5 4.0 5.0 \n", + "335 9.44 180 118 5 4.5 3.5 \n", + " 9.56 116 117 5 5.0 5.0 \n", + " 9.68 242 115 4 4.5 4.5 \n", + " 9.82 371 117 5 5.0 5.0 \n", + "336 9.19 274 118 5 4.5 4.0 \n", + " 9.53 185 118 5 4.5 5.0 \n", + " 9.62 358 119 4 4.5 4.0 \n", + " 9.76 68 112 5 5.0 5.0 \n", + " 9.80 22 119 5 4.0 3.5 \n", + "337 9.65 1 118 4 4.5 4.5 \n", + "338 9.23 348 115 5 4.5 5.0 \n", + " 9.40 29 118 4 3.0 4.5 \n", + " 9.46 103 117 4 3.5 4.5 \n", + " 9.66 203 120 4 5.0 5.0 \n", + "339 9.70 44 119 5 4.5 4.0 \n", + " 9.76 126 114 5 4.0 4.5 \n", + " 9.80 142 116 4 4.0 3.5 \n", + "340 9.45 81 115 5 4.5 4.5 \n", + " 9.50 78 120 4 5.0 5.0 \n", + " 9.60 30 114 5 4.0 4.0 \n", + " 9.66 272 112 4 5.0 4.5 \n", + " 9.74 370 113 4 5.0 5.0 \n", + " 9.91 193 120 5 4.5 4.5 \n", + " 9.92 137 120 4 4.5 4.0 \n", + "\n", + " Research Chance of Admit \n", + "GRE Score CGPA \n", + "290 7.46 0 0.45 \n", + " 7.56 0 0.47 \n", + "293 7.80 1 0.64 \n", + "294 7.36 0 0.46 \n", + " 7.64 0 0.49 \n", + "295 7.20 0 0.46 \n", + " 7.34 0 0.47 \n", + " 7.65 0 0.57 \n", + " 7.86 0 0.69 \n", + "296 7.28 0 0.47 \n", + " 7.54 1 0.44 \n", + " 7.68 0 0.60 \n", + " 7.80 0 0.49 \n", + " 8.03 0 0.61 \n", + "297 7.43 0 0.34 \n", + " 7.67 0 0.59 \n", + " 7.89 0 0.43 \n", + " 7.90 0 0.52 \n", + "298 7.46 0 0.53 \n", + " 7.50 1 0.44 \n", + " 7.60 0 0.46 \n", + " 7.86 0 0.54 \n", + " 7.88 0 0.51 \n", + " 8.03 0 0.34 \n", + " 8.54 0 0.69 \n", + "299 7.34 0 0.42 \n", + " 7.66 0 0.38 \n", + " 7.86 0 0.54 \n", + " 7.88 0 0.68 \n", + " 7.89 0 0.59 \n", + "... ... ... \n", + "334 9.07 1 0.89 \n", + " 9.43 1 0.93 \n", + " 9.48 1 0.94 \n", + " 9.54 1 0.93 \n", + " 9.70 1 0.95 \n", + " 9.87 1 0.97 \n", + "335 9.44 1 0.93 \n", + " 9.56 1 0.94 \n", + " 9.68 1 0.93 \n", + " 9.82 1 0.96 \n", + "336 9.19 1 0.92 \n", + " 9.53 1 0.94 \n", + " 9.62 1 0.95 \n", + " 9.76 1 0.96 \n", + " 9.80 1 0.97 \n", + "337 9.65 1 0.92 \n", + "338 9.23 1 0.91 \n", + " 9.40 1 0.91 \n", + " 9.46 1 0.91 \n", + " 9.66 1 0.95 \n", + "339 9.70 0 0.89 \n", + " 9.76 1 0.96 \n", + " 9.80 1 0.96 \n", + "340 9.45 1 0.94 \n", + " 9.50 1 0.96 \n", + " 9.60 1 0.90 \n", + " 9.66 1 0.94 \n", + " 9.74 1 0.96 \n", + " 9.91 1 0.97 \n", + " 9.92 1 0.97 \n", + "\n", + "[385 rows x 7 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admin2 = admissions.set_index([\"GRE Score\", \"CGPA\"]) #solito el set_index las quita del df principal\n", + "admin2.sort_index() #Consideramos que así sirve bastante para clasificar a los estudiantes\n", + "#pero debido a que si crece demasiado el número de estudiantes sube la probabilidad de que haya las \n", + "#mismas parejas y haya conflicto con los registros no recomendamos identificar de esta manera" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 2 - Advanced Functions\n", + "\n", + "In this part of the lab, we would like to test complex conditions on the entire dataset at once. Let's start by finding the number of rows where the CGPA is higher than 9 and the student has conducted research." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
CGPA
9.65133711844.54.59.6510.92
9.34533011554.53.09.3410.90
9.101132811244.04.59.1010.78
9.502032811655.05.09.5010.94
9.702133411955.04.59.7010.95
9.802233611954.03.59.8010.97
9.402933811843.04.59.4010.91
9.603034011454.04.09.6010.90
9.803133111254.05.09.8010.94
9.203232011055.05.09.2010.88
9.404132611354.54.09.4010.91
9.104232211055.04.09.1010.88
9.304332911454.05.09.3010.86
9.226531810933.54.09.2210.68
9.166632811544.54.09.1610.78
9.646733211855.05.09.6410.94
9.766833611255.05.09.7610.96
9.456932111155.05.09.4510.93
9.047031410844.54.09.0410.84
9.507834012045.05.09.5010.96
9.227932011055.04.59.2210.92
9.368032211554.04.59.3610.92
9.458134011554.54.59.4510.94
9.249433211945.04.59.2410.90
9.0510032611233.53.09.0510.74
9.1810232911144.54.59.1810.87
9.4610333811743.54.59.4610.91
9.3810433111655.05.09.3810.93
9.0411131010644.54.59.0410.66
9.5611633511755.05.09.5610.94
..............................
9.0423232511433.53.09.0410.76
9.0223432811044.02.59.0210.81
9.6824233511544.54.59.6810.93
9.3424733111945.04.59.3410.90
9.1324832710855.03.59.1310.87
9.4526432911355.04.59.4510.89
9.1126931711034.04.59.1110.80
9.6627234011245.04.59.6610.94
9.2627333111654.04.09.2610.93
9.1927433611854.54.09.1910.92
9.0827532411455.04.59.0810.89
9.2428633011434.54.59.2410.90
9.1029432311034.03.59.1010.79
9.1829932810844.54.09.1810.84
9.1431232611633.54.09.1410.81
9.4732333211855.05.09.4710.94
9.5434733411644.03.59.5410.93
9.2334833811554.55.09.2310.91
9.1735133011444.53.09.1710.86
9.2235732411033.53.09.2210.89
9.6235833611944.54.09.6210.95
9.1536832411044.54.09.1510.82
9.7437034011345.05.09.7410.96
9.8237133511755.05.09.8210.96
9.1237832611244.03.59.1210.84
9.2338032911144.54.09.2310.89
9.0438132411033.53.59.0410.82
9.1138232510733.03.59.1110.84
9.4538333011645.04.59.4510.91
9.6638533311745.04.09.6610.95
\n", + "

101 rows × 9 columns

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" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "CGPA \n", + "9.65 1 337 118 4 4.5 4.5 9.65 \n", + "9.34 5 330 115 5 4.5 3.0 9.34 \n", + "9.10 11 328 112 4 4.0 4.5 9.10 \n", + "9.50 20 328 116 5 5.0 5.0 9.50 \n", + "9.70 21 334 119 5 5.0 4.5 9.70 \n", + "9.80 22 336 119 5 4.0 3.5 9.80 \n", + "9.40 29 338 118 4 3.0 4.5 9.40 \n", + "9.60 30 340 114 5 4.0 4.0 9.60 \n", + "9.80 31 331 112 5 4.0 5.0 9.80 \n", + "9.20 32 320 110 5 5.0 5.0 9.20 \n", + "9.40 41 326 113 5 4.5 4.0 9.40 \n", + "9.10 42 322 110 5 5.0 4.0 9.10 \n", + "9.30 43 329 114 5 4.0 5.0 9.30 \n", + "9.22 65 318 109 3 3.5 4.0 9.22 \n", + "9.16 66 328 115 4 4.5 4.0 9.16 \n", + "9.64 67 332 118 5 5.0 5.0 9.64 \n", + "9.76 68 336 112 5 5.0 5.0 9.76 \n", + "9.45 69 321 111 5 5.0 5.0 9.45 \n", + "9.04 70 314 108 4 4.5 4.0 9.04 \n", + "9.50 78 340 120 4 5.0 5.0 9.50 \n", + "9.22 79 320 110 5 5.0 4.5 9.22 \n", + "9.36 80 322 115 5 4.0 4.5 9.36 \n", + "9.45 81 340 115 5 4.5 4.5 9.45 \n", + "9.24 94 332 119 4 5.0 4.5 9.24 \n", + "9.05 100 326 112 3 3.5 3.0 9.05 \n", + "9.18 102 329 111 4 4.5 4.5 9.18 \n", + "9.46 103 338 117 4 3.5 4.5 9.46 \n", + "9.38 104 331 116 5 5.0 5.0 9.38 \n", + "9.04 111 310 106 4 4.5 4.5 9.04 \n", + "9.56 116 335 117 5 5.0 5.0 9.56 \n", + "... ... ... ... ... ... ... ... \n", + "9.04 232 325 114 3 3.5 3.0 9.04 \n", + "9.02 234 328 110 4 4.0 2.5 9.02 \n", + "9.68 242 335 115 4 4.5 4.5 9.68 \n", + "9.34 247 331 119 4 5.0 4.5 9.34 \n", + "9.13 248 327 108 5 5.0 3.5 9.13 \n", + "9.45 264 329 113 5 5.0 4.5 9.45 \n", + "9.11 269 317 110 3 4.0 4.5 9.11 \n", + "9.66 272 340 112 4 5.0 4.5 9.66 \n", + "9.26 273 331 116 5 4.0 4.0 9.26 \n", + "9.19 274 336 118 5 4.5 4.0 9.19 \n", + "9.08 275 324 114 5 5.0 4.5 9.08 \n", + "9.24 286 330 114 3 4.5 4.5 9.24 \n", + "9.10 294 323 110 3 4.0 3.5 9.10 \n", + "9.18 299 328 108 4 4.5 4.0 9.18 \n", + "9.14 312 326 116 3 3.5 4.0 9.14 \n", + "9.47 323 332 118 5 5.0 5.0 9.47 \n", + "9.54 347 334 116 4 4.0 3.5 9.54 \n", + "9.23 348 338 115 5 4.5 5.0 9.23 \n", + "9.17 351 330 114 4 4.5 3.0 9.17 \n", + "9.22 357 324 110 3 3.5 3.0 9.22 \n", + "9.62 358 336 119 4 4.5 4.0 9.62 \n", + "9.15 368 324 110 4 4.5 4.0 9.15 \n", + "9.74 370 340 113 4 5.0 5.0 9.74 \n", + "9.82 371 335 117 5 5.0 5.0 9.82 \n", + "9.12 378 326 112 4 4.0 3.5 9.12 \n", + "9.23 380 329 111 4 4.5 4.0 9.23 \n", + "9.04 381 324 110 3 3.5 3.5 9.04 \n", + "9.11 382 325 107 3 3.0 3.5 9.11 \n", + "9.45 383 330 116 4 5.0 4.5 9.45 \n", + "9.66 385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit \n", + "CGPA \n", + "9.65 1 0.92 \n", + "9.34 1 0.90 \n", + "9.10 1 0.78 \n", + "9.50 1 0.94 \n", + "9.70 1 0.95 \n", + "9.80 1 0.97 \n", + "9.40 1 0.91 \n", + "9.60 1 0.90 \n", + "9.80 1 0.94 \n", + "9.20 1 0.88 \n", + "9.40 1 0.91 \n", + "9.10 1 0.88 \n", + "9.30 1 0.86 \n", + "9.22 1 0.68 \n", + "9.16 1 0.78 \n", + "9.64 1 0.94 \n", + "9.76 1 0.96 \n", + "9.45 1 0.93 \n", + "9.04 1 0.84 \n", + "9.50 1 0.96 \n", + "9.22 1 0.92 \n", + "9.36 1 0.92 \n", + "9.45 1 0.94 \n", + "9.24 1 0.90 \n", + "9.05 1 0.74 \n", + "9.18 1 0.87 \n", + "9.46 1 0.91 \n", + "9.38 1 0.93 \n", + "9.04 1 0.66 \n", + "9.56 1 0.94 \n", + "... ... ... \n", + "9.04 1 0.76 \n", + "9.02 1 0.81 \n", + "9.68 1 0.93 \n", + "9.34 1 0.90 \n", + "9.13 1 0.87 \n", + "9.45 1 0.89 \n", + "9.11 1 0.80 \n", + "9.66 1 0.94 \n", + "9.26 1 0.93 \n", + "9.19 1 0.92 \n", + "9.08 1 0.89 \n", + "9.24 1 0.90 \n", + "9.10 1 0.79 \n", + "9.18 1 0.84 \n", + "9.14 1 0.81 \n", + "9.47 1 0.94 \n", + "9.54 1 0.93 \n", + "9.23 1 0.91 \n", + "9.17 1 0.86 \n", + "9.22 1 0.89 \n", + "9.62 1 0.95 \n", + "9.15 1 0.82 \n", + "9.74 1 0.96 \n", + "9.82 1 0.96 \n", + "9.12 1 0.84 \n", + "9.23 1 0.89 \n", + "9.04 1 0.82 \n", + "9.11 1 0.84 \n", + "9.45 1 0.91 \n", + "9.66 1 0.95 \n", + "\n", + "[101 rows x 9 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admissions[(admissions.CGPA > 9) & (admissions.Research == 1)] #101 registros\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now return all the rows where the CGPA is greater than 9 and the SOP score is less than 3.5. Find the mean chance of admit for these applicants." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8019999999999999" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "#En una linea\n", + "admissions[(admissions.CGPA > 9) & (admissions.SOP < 3.5)]['Chance of Admit'].mean()\n", + "\n", + "#Por pasos\n", + "admin1 = admissions[(admissions.CGPA > 9) & (admissions.SOP < 3.5)]\n", + "admin1['Chance of Admit'].mean() #0.8019" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would like to create a deciding factor column for each student. We standardize several columns and then pick the most important factor from a lookup table. If the standardized value is above 0.8, the student will be accepted.\n", + "\n", + "We will start by creating a standardized column for `CGPA`, `GRE Score` and `LOR`. We will name these columns `CGPA_std`, `GRE_std`, and `LOR_std` respecively.\n", + "\n", + "Recall that standardizing a column is done by subtracting the mean of the column from all observations in the column and then dividing each observation in the column by the column's standard deviation.\n", + "\n", + "In the cell below, write a standardization function (a function that takes a column as input and returns a standardized column as output). Make sure to use the `numpy` versions of mean and standard deviation." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def standardize(col):\n", + " # This function takes a column from a dataframe and returns a standardized column by subtracting the column's mean\n", + " # and dividing by the column's standard deviation.\n", + " \n", + " # Your code here:\n", + " column_mean = np.mean(col)\n", + " column_std = np.std(col)\n", + " standarized_column = np.absolute(col - column_mean)/column_std \n", + " return standarized_column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the standardized columns `CGPA_std`, `GRE_std`, and `LOR_std` and add them to the `admissions` dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8.597090909090909 0.6023853232371412\n" + ] + }, + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitCGPA_stdGRE_stdLOR_std
CGPA
9.65133711844.54.59.6510.921.7501741.7556631.193197
8.00231610433.03.58.0010.720.9925010.0634500.076840
8.67332211033.52.58.6710.800.1211910.4562971.039517
8.21431410322.03.08.2100.650.6434330.2366980.481338
9.34533011554.53.09.3410.901.2348841.1492920.481338
\n", + "
" + ], + "text/plain": [ + " Serial No. GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "CGPA \n", + "9.65 1 337 118 4 4.5 4.5 9.65 \n", + "8.00 2 316 104 3 3.0 3.5 8.00 \n", + "8.67 3 322 110 3 3.5 2.5 8.67 \n", + "8.21 4 314 103 2 2.0 3.0 8.21 \n", + "9.34 5 330 115 5 4.5 3.0 9.34 \n", + "\n", + " Research Chance of Admit CGPA_std GRE_std LOR_std \n", + "CGPA \n", + "9.65 1 0.92 1.750174 1.755663 1.193197 \n", + "8.00 1 0.72 0.992501 0.063450 0.076840 \n", + "8.67 1 0.80 0.121191 0.456297 1.039517 \n", + "8.21 0 0.65 0.643433 0.236698 0.481338 \n", + "9.34 1 0.90 1.234884 1.149292 0.481338 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "#Este pedazo es para obtener datos que verifiquen que la función de arriba si funciona bien\n", + "CGPA_mean = admissions['CGPA'].mean() #8.597090909090909\n", + "CGPA_std = admissions['CGPA'].std() #0.6023853232371412\n", + "print(CGPA_mean,CGPA_std)\n", + "\n", + "\n", + "admissions['CGPA_std'] = standardize(admissions.CGPA)\n", + "admissions['GRE_std'] = standardize(admissions['GRE Score'])\n", + "admissions['LOR_std'] = standardize(admissions.LOR)\n", + "admissions.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will generate the decision choice at random using the code below. Please run the cell." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this code:\n", + "\n", + "from random import choices\n", + "std_columns = ['CGPA_std', 'GRE_std', 'LOR_std']\n", + "\n", + "decision_choice = choices(std_columns, k=admissions.shape[0]) #es una lista\n", + "#decision_choice" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the deciding column using the `lookup` function. The lookup column is `decision_choice` found above. Call the column resulting from the lookup function `deciding_column` and add it to the `admissions` dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CGPA\n", + "9.65 1.75017\n", + "8.00 [0.06344955097704703, 1.2761909685879955, 0.84...\n", + "8.67 [0.45629677085621656, 0.10979922296737417, 0.5...\n", + "8.21 [0.23669832492146825, 0.7564446467547319]\n", + "9.34 [0.48133840104963, 1.1931973013971253]\n", + "8.20 [0.6600552885787807, 0.6600552885787807, 0.660...\n", + "7.90 [1.1587234324226374, 1.1587234324226374, 1.158...\n", + "8.00 [0.9925007178080185, 0.9925007178080185, 0.992...\n", + "8.60 [0.5429211578284272, 0.7564446467547319]\n", + "9.00 [0.6697264283381763, 0.6697264283381763, 0.669...\n", + "9.10 [0.8359491429527952, 0.8359491429527952, 0.835...\n", + "8.00 [0.07684016643262176, 2.155874103496385, 0.481...\n", + "8.20 [0.6600552885787807, 0.6600552885787807, 0.660...\n", + "8.30 [1.0395169685318817, 0.6350187339148735, 1.193...\n", + "8.70 [0.48133840104963, 0.07684016643262176]\n", + "8.00 [0.07684016643262176, 2.155874103496385, 0.481...\n", + "8.80 [0.3372809991089385, 0.3372809991089385, 0.337...\n", + "8.50 [1.189566581615785, 0.32332271189367884, 0.150...\n", + "8.40 [1.5976955360141336, 0.6350187339148735, 0.635...\n", + "9.50 [1.751375868879377, 1.751375868879377]\n", + "9.70 [1.1931973013971253, 0.6350187339148735]\n", + "9.80 [0.07684016643262176, 1.751375868879377, 0.076...\n", + "8.80 [0.3372809991089385, 0.3372809991089385, 0.337...\n", + "7.50 [1.6226885164768379, 1.1029421946435742]\n", + "7.20 1.88256\n", + "7.30 2.15606\n", + "8.10 [1.4494397425324168, 0.6295455448006378, 0.669...\n", + "8.30 [0.4938325739641589, 0.4938325739641589, 0.493...\n", + "9.40 [1.1931973013971253, 0.6350187339148735]\n", + "9.60 0.635019\n", + " ... \n", + "8.24 [0.7564446467547319, 0.5831958728103107, 0.236...\n", + "9.22 [0.10979922296737417, 0.2830479969117954, 0.62...\n", + "9.62 1.70031\n", + "8.54 [0.023174835995163567, 1.6226885164768379, 0.1...\n", + "7.65 [0.48133840104963, 0.48133840104963, 1.0395169...\n", + "7.66 [1.5576579474977246, 1.5576579474977246, 1.557...\n", + "7.43 [1.93997019111135, 1.93997019111135]\n", + "7.56 2.31568\n", + "7.65 [1.5742802189591862, 1.5742802189591862, 1.574...\n", + "8.43 [0.27774304496515523, 0.27774304496515523]\n", + "8.84 [0.4037700849547849, 0.4037700849547849]\n", + "8.67 [0.45629677085621656, 0.10979922296737417, 0.5...\n", + "9.15 [0.9190605002601061, 0.9190605002601061]\n", + "8.26 [0.48133840104963, 1.0395169685318817, 1.03951...\n", + "9.74 2.01554\n", + "9.82 1.75138\n", + "7.96 0.0768402\n", + "8.10 [1.4494397425324168, 0.6295455448006378, 0.669...\n", + "7.80 [1.3249461470372579, 1.3249461470372579, 1.324...\n", + "8.44 [0.26112077350369367, 0.26112077350369367, 0.2...\n", + "8.24 [0.7564446467547319, 0.5831958728103107, 0.236...\n", + "8.65 [1.189566581615785, 0.32332271189367884, 1.016...\n", + "9.12 [0.10979922296737417, 0.369672383884006, 0.802...\n", + "8.76 [0.06344955097704703, 0.6295455448006378, 0.71...\n", + "9.23 [1.1931973013971253, 0.6350187339148735, 1.751...\n", + "9.04 [0.6350187339148735, 1.1931973013971253, 0.076...\n", + "9.11 [0.48133840104963, 1.1931973013971253, 1.19319...\n", + "9.45 [0.369672383884006, 2.0155357363560076, 1.0626...\n", + "8.78 [1.1931973013971253, 1.1931973013971253, 0.635...\n", + "9.66 [1.751375868879377, 1.1931973013971253, 0.6350...\n", + "Name: deciding_column, Length: 385, dtype: object" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "admissions['deciding_column'] = admissions.lookup(admissions.index, decision_choice)\n", + "#df.lookup(row labels, column labels)\n", + "admissions.deciding_column" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a column called `decision` in the `admissions` dataframe. Assign 1 to this column if the value of `deciding_column` is greater than 0.8 and 0 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "admissions['decision'] = np.where ...\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many applicants will be accepted to the program using the decision column? Compute the result below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3 - Method Chaining\n", + "\n", + "To increase our coding efficiency, let's make a number of changes to our dataframe in one line of code.\n", + "\n", + "In the cell below, remove all non character symbols from the column names, replace all spaces in column names to underscores, and change all upper case characters to lower case. Assign these new column values to `admissions.columns`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, give all student with a university rating of 4 or higher a 10 point boost on their GRE score and split the column into 4 bins using the `cut` function. Assign this new score to the variable `adjusted_gre`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..bbea1c6 --- /dev/null +++ b/your-code/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,356 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Before your start:\n", + "- Read the README.md file\n", + "- Comment as much as you can and use the resources (README.md file)\n", + "- Happy learning!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import numpy and pandas\n", + "import pandas as pd\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1 - Index Functions\n", + "\n", + "In this challenge, we will practice the advanced functions introduced in the lesson. However, before we start, we will load and evaluate our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this code:\n", + "\n", + "admissions = pd.read_csv('../Admission_Predict.csv')\n", + "#to remove trailing spaces at the end of the column names\n", + "admissions.columns = admissions.columns.str.rstrip()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's evaluate the dataset by looking at the `head` function." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before beginning to work with this dataset and evaluating graduate admissions data, we will verify that there is no missing data in the dataset. Do this in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interestingly, there is a column that uniquely identifies the applicants. This column is the serial number column. Instead of having our own index, we should make this column our index. Do this in the cell below. Keep the column in the dataframe in addition to making it an index." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Turns out that `GRE Score` and `CGPA` also uniquely identify the data. Show this in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the index with an index comprised of two columns - `GRE Score` and `CGPA`. Remove the columns from the dataset as well. Make sure to use `inplace=True`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now change the index back to a sequence starting at zero using the `reset_index` function. Make sure to use `inplace=True`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 2 - Advanced Functions\n", + "\n", + "In this part of the lab, we would like to test complex conditions on the entire dataset at once. Let's start by finding the number of rows where the CGPA is higher than 9 and the student has conducted research." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now return all the rows where the CGPA is greater than 9 and the SOP score is less than 3.5. Find the mean chance of admit for these applicants." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would like to create a deciding factor column for each student. We standardize several columns and then pick the most important factor from a lookup table. If the standardized value is above 0.8, the student will be accepted.\n", + "\n", + "We will start by creating a standardized column for `CGPA`, `GRE Score` and `LOR`. We will name these columns `CGPA_std`, `GRE_std`, and `LOR_std` respecively.\n", + "\n", + "Recall that standardizing a column is done by subtracting the mean of the column from all observations in the column and then dividing each observation in the column by the column's standard deviation.\n", + "\n", + "In the cell below, write a standardization function (a function that takes a column as input and returns a standardized column as output). Make sure to use the `numpy` versions of mean and standard deviation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def standardize(col):\n", + " # This function takes a column from a dataframe and returns a standardized column by subtracting the column's mean\n", + " # and dividing by the column's standard deviation.\n", + " \n", + " # Your code here:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the standardized columns `CGPA_std`, `GRE_std`, and `LOR_std` and add them to the `admissions` dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will generate the decision choice at random using the code below. Please run the cell." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this code:\n", + "\n", + "from random import choices\n", + "std_columns = ['CGPA_std', 'GRE_std', 'LOR_std']\n", + "\n", + "decision_choice = choices(std_columns, k=admissions.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now create the deciding column using the `lookup` function. The lookup column is `decision_choice` found above. Call the column resulting from the lookup function `deciding_column` and add it to the `admissions` dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a column called `decision` in the `admissions` dataframe. Assign 1 to this column if the value of `deciding_column` is greater than 0.8 and 0 otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How many applicants will be accepted to the program using the decision column? Compute the result below." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3 - Method Chaining\n", + "\n", + "To increase our coding efficiency, let's make a number of changes to our dataframe in one line of code.\n", + "\n", + "In the cell below, remove all non character symbols from the column names, replace all spaces in column names to underscores, and change all upper case characters to lower case. Assign these new column values to `admissions.columns`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the cell below, give all student with a university rating of 4 or higher a 10 point boost on their GRE score and split the column into 4 bins using the `cut` function. Assign this new score to the variable `adjusted_gre`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/your-code/main.ipynb b/your-code/main.ipynb index f1c6ee0..0fa0160 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,12 +12,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# import numpy and pandas\n", - "\n" + "import pandas as pd\n", + "import numpy as np\n" ] }, { @@ -31,15 +32,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "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
..............................
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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": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Run this code:\n", "\n", "admissions = pd.read_csv('../Admission_Predict.csv')\n", "#to remove trailing spaces at the end of the column names\n", - "admissions.columns = admissions.columns.str.rstrip()" + "admissions.columns = admissions.columns.str.rstrip()\n", + "admissions" ] }, { @@ -51,12 +258,130 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "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
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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": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "admissions.head()\n" ] }, { @@ -68,12 +393,32 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, - "outputs": [], + "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": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "admissions.isnull().sum()\n" ] }, { @@ -85,12 +430,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "admissions.index = admissions['Serial No.']\n" ] }, { @@ -102,12 +447,235 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
CGPA
6.80553009913.02.06.8010.36
7.20252959312.02.07.2000.46
7.253343029912.02.07.2500.57
7.281142969923.03.57.2800.47
7.30263109921.52.07.3000.54
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9.9119334012054.54.59.9110.97
9.9213734012044.54.09.9210.97
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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", + "CGPA \n", + "6.80 55 300 99 1 3.0 2.0 6.80 \n", + "7.20 25 295 93 1 2.0 2.0 7.20 \n", + "7.25 334 302 99 1 2.0 2.0 7.25 \n", + "7.28 114 296 99 2 3.0 3.5 7.28 \n", + "7.30 26 310 99 2 1.5 2.0 7.30 \n", + "... ... ... ... ... ... ... ... \n", + "9.80 22 336 119 5 4.0 3.5 9.80 \n", + "9.82 371 335 117 5 5.0 5.0 9.82 \n", + "9.87 194 334 120 5 4.0 5.0 9.87 \n", + "9.91 193 340 120 5 4.5 4.5 9.91 \n", + "9.92 137 340 120 4 4.5 4.0 9.92 \n", + "\n", + " Research Chance of Admit \n", + "CGPA \n", + "6.80 1 0.36 \n", + "7.20 0 0.46 \n", + "7.25 0 0.57 \n", + "7.28 0 0.47 \n", + "7.30 0 0.54 \n", + "... ... ... \n", + "9.80 1 0.97 \n", + "9.82 1 0.96 \n", + "9.87 1 0.97 \n", + "9.91 1 0.97 \n", + "9.92 1 0.97 \n", + "\n", + "[385 rows x 9 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "#admissions.index = admissions['GRE Score']\n", + "admissions.index = admissions['CGPA']\n", + "\n", + "admissions.sort_index() #El GRE no es un identificador válido porque varios tienen la misma evaluación\n", + "#El cgps tampoco es un identificador válido" ] }, { @@ -126,12 +694,220 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.TOEFL ScoreUniversity RatingSOPLORResearchChance of Admit
CGPAGRE Score
9.65337111844.54.510.92
8.00316210433.03.510.72
8.67322311033.52.510.80
8.21314410322.03.000.65
9.34330511554.53.010.90
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385 rows × 7 columns

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" + ], + "text/plain": [ + " Serial No. TOEFL Score University Rating SOP LOR \\\n", + "CGPA GRE Score \n", + "9.65 337 1 118 4 4.5 4.5 \n", + "8.00 316 2 104 3 3.0 3.5 \n", + "8.67 322 3 110 3 3.5 2.5 \n", + "8.21 314 4 103 2 2.0 3.0 \n", + "9.34 330 5 115 5 4.5 3.0 \n", + "... ... ... ... ... ... \n", + "9.04 324 381 110 3 3.5 3.5 \n", + "9.11 325 382 107 3 3.0 3.5 \n", + "9.45 330 383 116 4 5.0 4.5 \n", + "8.78 312 384 103 3 3.5 4.0 \n", + "9.66 333 385 117 4 5.0 4.0 \n", + "\n", + " Research Chance of Admit \n", + "CGPA GRE Score \n", + "9.65 337 1 0.92 \n", + "8.00 316 1 0.72 \n", + "8.67 322 1 0.80 \n", + "8.21 314 0 0.65 \n", + "9.34 330 1 0.90 \n", + "... ... ... \n", + "9.04 324 1 0.82 \n", + "9.11 325 1 0.84 \n", + "9.45 330 1 0.91 \n", + "8.78 312 0 0.67 \n", + "9.66 333 1 0.95 \n", + "\n", + "[385 rows x 7 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "admissions.set_index([\"CGPA\", \"GRE Score\"])\n", + "\n", + "#df.eval('D=(A+B)/C', inplace=True)" ] }, { @@ -145,12 +921,33 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Serial No. 101\n", + "GRE Score 101\n", + "TOEFL Score 101\n", + "University Rating 101\n", + "SOP 101\n", + "LOR 101\n", + "CGPA 101\n", + "Research 101\n", + "Chance of Admit 101\n", + "dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "\n", + "admissions[(admissions.CGPA > 9) & (admissions.Research == 1)].count()" ] }, { @@ -162,11 +959,24 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.8019999999999999" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", + "admit = admissions[(admissions.CGPA>9)&(admissions.SOP<3.5)]\n", + "admit['Chance of Admit'].mean()\n", "\n" ] }, @@ -185,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -193,7 +1003,10 @@ " # This function takes a column from a dataframe and returns a standardized column by subtracting the column's mean\n", " # and dividing by the column's standard deviation.\n", " \n", - " # Your code here:" + " # Your code here:\n", + " colmean = col.mean()\n", + " colstd = col.std() \n", + " return (col - colmean)/colstd" ] }, { @@ -205,12 +1018,17 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "CGPA_std = standardize(admissions.CGPA)\n", + "GRE_std = standardize(admissions['GRE Score'])\n", + "LOR_std = standardize(admissions.LOR)\n", + "admissions['CGPA_std'] = CGPA_std\n", + "admissions['GRE_std'] = GRE_std\n", + "admissions['LOR_std'] = LOR_std\n" ] }, { @@ -222,7 +1040,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -243,12 +1061,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "\n", + "deciding_column = admissions.lookup(admissions.index, decision_choice)\n", + "#deciding_column" ] }, { @@ -260,12 +1080,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "\n", + "admissions['deciding_column'] = deciding_column\n", + "#admissions['decision'] = admissions['deciding_column'].map(lambda x: 1 if x > 0.8 else 0)\n" ] }, { @@ -298,12 +1120,185 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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serial_nogre_scoretoefl_scoreuniversity_ratingsoplorcgparesearchchance_of_admitcgpa_stdgre_stdlor_stddeciding_column
CGPA
9.65133711844.54.59.6510.921.7479001.7533811.1916471.75338
8.00231610433.03.58.0010.72-0.991211-0.0633670.076740[-0.063367095385386, -1.2745325000918912, -0.8...
8.67332211033.52.58.6710.800.1210340.455704-1.038166[0.45570379234597336, 0.10965653385840046, -0....
8.21431410322.03.08.2100.65-0.642597-0.236391-0.480713[-0.48071287979634203, -1.0381660687167988]
9.34533011554.53.09.3410.901.2332791.147798-0.480713[1.1477983093211193, 1.2343101239430125]
\n", + "
" + ], + "text/plain": [ + " serial_no gre_score toefl_score university_rating sop lor cgpa \\\n", + "CGPA \n", + "9.65 1 337 118 4 4.5 4.5 9.65 \n", + "8.00 2 316 104 3 3.0 3.5 8.00 \n", + "8.67 3 322 110 3 3.5 2.5 8.67 \n", + "8.21 4 314 103 2 2.0 3.0 8.21 \n", + "9.34 5 330 115 5 4.5 3.0 9.34 \n", + "\n", + " research chance_of_admit cgpa_std gre_std lor_std \\\n", + "CGPA \n", + "9.65 1 0.92 1.747900 1.753381 1.191647 \n", + "8.00 1 0.72 -0.991211 -0.063367 0.076740 \n", + "8.67 1 0.80 0.121034 0.455704 -1.038166 \n", + "8.21 0 0.65 -0.642597 -0.236391 -0.480713 \n", + "9.34 1 0.90 1.233279 1.147798 -0.480713 \n", + "\n", + " deciding_column \n", + "CGPA \n", + "9.65 1.75338 \n", + "8.00 [-0.063367095385386, -1.2745325000918912, -0.8... \n", + "8.67 [0.45570379234597336, 0.10965653385840046, -0.... \n", + "8.21 [-0.48071287979634203, -1.0381660687167988] \n", + "9.34 [1.1477983093211193, 1.2343101239430125] " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "import re \n", + "\n", + "admissions.columns = admissions.columns.str.replace(\" \",\"_\").str.lower().str.replace(r'[.]\\s?',r'',regex=True)\n", + "admissions.columns\n", + "admissions.head()\n", + "#necesito reforzar regex" ] }, { @@ -315,12 +1310,197 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# Your code here:\n", - "\n" + "\n", + "admissions[\"adjusted_gre\"] = np.where(admissions[\"university_rating\"] >=4, admissions['gre_score']+10, admissions[\"gre_score\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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serial_nogre_scoretoefl_scoreuniversity_ratingsoplorcgparesearchchance_of_admitcgpa_stdgre_stdlor_stddeciding_columnadjusted_gre
CGPA
9.65133711844.54.59.6510.921.7479001.7533811.1916471.75338347
8.00231610433.03.58.0010.72-0.991211-0.0633670.076740[-0.063367095385386, -1.2745325000918912, -0.8...316
8.67332211033.52.58.6710.800.1210340.455704-1.038166[0.45570379234597336, 0.10965653385840046, -0....322
8.21431410322.03.08.2100.65-0.642597-0.236391-0.480713[-0.48071287979634203, -1.0381660687167988]314
9.34533011554.53.09.3410.901.2332791.147798-0.480713[1.1477983093211193, 1.2343101239430125]340
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" + ], + "text/plain": [ + " serial_no gre_score toefl_score university_rating sop lor cgpa \\\n", + "CGPA \n", + "9.65 1 337 118 4 4.5 4.5 9.65 \n", + "8.00 2 316 104 3 3.0 3.5 8.00 \n", + "8.67 3 322 110 3 3.5 2.5 8.67 \n", + "8.21 4 314 103 2 2.0 3.0 8.21 \n", + "9.34 5 330 115 5 4.5 3.0 9.34 \n", + "\n", + " research chance_of_admit cgpa_std gre_std lor_std \\\n", + "CGPA \n", + "9.65 1 0.92 1.747900 1.753381 1.191647 \n", + "8.00 1 0.72 -0.991211 -0.063367 0.076740 \n", + "8.67 1 0.80 0.121034 0.455704 -1.038166 \n", + "8.21 0 0.65 -0.642597 -0.236391 -0.480713 \n", + "9.34 1 0.90 1.233279 1.147798 -0.480713 \n", + "\n", + " deciding_column adjusted_gre \n", + "CGPA \n", + "9.65 1.75338 347 \n", + "8.00 [-0.063367095385386, -1.2745325000918912, -0.8... 316 \n", + "8.67 [0.45570379234597336, 0.10965653385840046, -0.... 322 \n", + "8.21 [-0.48071287979634203, -1.0381660687167988] 314 \n", + "9.34 [1.1477983093211193, 1.2343101239430125] 340 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.head()" ] }, { @@ -347,7 +1527,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.3" } }, "nbformat": 4,