diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..a567b1c 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -44,10 +44,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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\n" + ] + } + ], + "source": [ + "series_lst = pd.Series(lst)\n", + "print(series_lst)" + ] }, { "cell_type": "markdown", @@ -60,10 +81,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_lst[2]" + ] }, { "cell_type": "markdown", @@ -74,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +126,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4\n", + "0 53.1 95.0 67.5 35.0 78.4\n", + "1 61.3 40.8 30.8 37.8 87.6\n", + "2 20.6 73.2 44.2 14.6 91.8\n", + "3 57.4 0.1 96.1 4.2 69.5\n", + "4 83.6 20.5 85.4 22.8 35.9\n", + "5 49.0 69.0 0.1 31.8 89.1\n", + "6 23.3 40.7 95.0 83.8 26.9\n", + "7 27.6 26.4 53.8 88.8 68.5\n", + "8 96.6 96.4 53.4 72.4 50.1\n", + "9 73.7 39.0 43.2 81.6 34.7\n" + ] + } + ], + "source": [ + "df_b = pd.DataFrame(b)\n", + "print(df_b)" + ] }, { "cell_type": "markdown", @@ -124,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -133,10 +188,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Score_1 Score_2 Score_3 Score_4 Score_5\n", + "0 53.1 95.0 67.5 35.0 78.4\n", + "1 61.3 40.8 30.8 37.8 87.6\n", + "2 20.6 73.2 44.2 14.6 91.8\n", + "3 57.4 0.1 96.1 4.2 69.5\n", + "4 83.6 20.5 85.4 22.8 35.9\n", + "5 49.0 69.0 0.1 31.8 89.1\n", + "6 23.3 40.7 95.0 83.8 26.9\n", + "7 27.6 26.4 53.8 88.8 68.5\n", + "8 96.6 96.4 53.4 72.4 50.1\n", + "9 73.7 39.0 43.2 81.6 34.7\n" + ] + } + ], + "source": [ + "df_b.columns = colnames\n", + "print(df_b)" + ] }, { "cell_type": "markdown", @@ -147,10 +223,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
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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": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.head()" + ] }, { "cell_type": "markdown", @@ -340,39 +890,310 @@ "source": [] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 67, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No duplicates found. Combining GRE Score and CGPA uniquely identifies the data.\n" + ] + } + ], "source": [ - "### 3 - In this part of the lab, we would like to test complex conditions on the entire data set at once. Let's start by finding the number of rows where the CGPA is greater than 9 and the student has performed an investigation." + "df_duplicates = pd.DataFrame(admissions)\n", + "\n", + "duplicates = df_duplicates[df_duplicates.duplicated(subset=['GRE Score', 'CGPA'], keep=False)]\n", + "\n", + "if duplicates.empty:\n", + " print(\"No duplicates found. Combining GRE Score and CGPA uniquely identifies the data.\")\n", + "else:\n", + " print(\"Duplicates found. GRE Score and CGPA do not uniquely identify the data.\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions['CGPA'].is_unique" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4 - 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." + "### 3 - In this part of the lab, we would like to test complex conditions on the entire data set at once. Let's start by finding the number of rows where the CGPA is greater than 9 and the student has performed an investigation." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
Serial No.
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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", + "5 5 330 115 5 4.5 3.0 \n", + "11 11 328 112 4 4.0 4.5 \n", + "20 20 328 116 5 5.0 5.0 \n", + "21 21 334 119 5 5.0 4.5 \n", + "... ... ... ... ... ... ... \n", + "380 380 329 111 4 4.5 4.0 \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", + "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", + "5 9.34 1 0.90 \n", + "11 9.10 1 0.78 \n", + "20 9.50 1 0.94 \n", + "21 9.70 1 0.95 \n", + "... ... ... ... \n", + "380 9.23 1 0.89 \n", + "381 9.04 1 0.82 \n", + "382 9.11 1 0.84 \n", + "383 9.45 1 0.91 \n", + "385 9.66 1 0.95 \n", + "\n", + "[101 rows x 9 columns]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions[(admissions['CGPA'] > 9) & (admissions['Research'] == True)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4 - 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": null, + "execution_count": 81, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.8019999999999999" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions[(admissions['CGPA'] > 9) & (admissions['SOP'] < 3.5)]['Chance of Admit'].mean()" + ] }, { "cell_type": "markdown", @@ -384,10 +1205,249 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitTOEFL Grater than 100
Serial No.
1133711844.54.59.6510.92True
2231610433.03.58.0010.72True
3332211033.52.58.6710.80True
4431410322.03.08.2100.65True
5533011554.53.09.3410.90True
.................................
38138132411033.53.59.0410.82True
38238232510733.03.59.1110.84True
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38438431210333.54.08.7800.67True
38538533311745.04.09.6610.95True
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitTOEFL Grater than 100Decision
Serial No.
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5533011554.53.09.3410.90TrueYes
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitTOEFL Grater than 100DecisionDecision2
Serial No.
1133711844.54.59.6510.92TrueYes1
2231610433.03.58.0010.72TrueYes0
3332211033.52.58.6710.80TrueYes1
4431410322.03.08.2100.65TrueYes0
5533011554.53.09.3410.90TrueYes1
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38138132411033.53.59.0410.82TrueYes1
38238232510733.03.59.1110.84TrueYes0
38338333011645.04.59.4510.91TrueYes1
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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", + "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 TOEFL Grater than 100 Decision \\\n", + "Serial No. \n", + "1 9.65 1 0.92 True Yes \n", + "2 8.00 1 0.72 True Yes \n", + "3 8.67 1 0.80 True Yes \n", + "4 8.21 0 0.65 True Yes \n", + "5 9.34 1 0.90 True Yes \n", + "... ... ... ... ... ... \n", + "381 9.04 1 0.82 True Yes \n", + "382 9.11 1 0.84 True Yes \n", + "383 9.45 1 0.91 True Yes \n", + "384 8.78 0 0.67 True Yes \n", + "385 9.66 1 0.95 True Yes \n", + "\n", + " Decision2 \n", + "Serial No. \n", + "1 1 \n", + "2 0 \n", + "3 1 \n", + "4 0 \n", + "5 1 \n", + "... ... \n", + "381 1 \n", + "382 0 \n", + "383 1 \n", + "384 1 \n", + "385 1 \n", + "\n", + "[385 rows x 12 columns]" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions" + ] } ], "metadata": { @@ -449,7 +2034,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "toc": { "base_numbering": "",