diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..d6e1f1d 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -44,10 +44,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "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": [ + "lst = pd.Series([5.7, 75.2, 74.4, 84.0, 66.5, 66.3, 55.8, 75.7, 29.1, 43.7])\n", + "print(lst)" + ] }, { "cell_type": "markdown", @@ -60,10 +81,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + } + ], + "source": [ + "lst.index\n", + "\n", + "for i in lst.index:\n", + " print(i)" + ] }, { "cell_type": "markdown", @@ -74,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +135,145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Score_1 Score_2 Score_3 Score_4 Score_5\n", + "0 53.1 95.0 67.5 35.0 78.4\n", + "1 61.3 40.8 30.8 37.8 87.6\n", + "2 20.6 73.2 44.2 14.6 91.8\n", + "3 57.4 0.1 96.1 4.2 69.5\n", + "4 83.6 20.5 85.4 22.8 35.9\n", + "5 49.0 69.0 0.1 31.8 89.1\n", + "6 23.3 40.7 95.0 83.8 26.9\n", + "7 27.6 26.4 53.8 88.8 68.5\n", + "8 96.6 96.4 53.4 72.4 50.1\n", + "9 73.7 39.0 43.2 81.6 34.7" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns = colnames\n", + "df.rename(columns={'0':'Score_1', '1':'Score_2', '2':'Score_3', '3':'Score_4', '4':'Score_5'}, inplace=False)\n", + "df" + ] }, { "cell_type": "markdown", @@ -147,10 +461,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Score_1 Score_3 Score_5\n", + "0 53.1 67.5 78.4\n", + "1 61.3 30.8 87.6\n", + "2 20.6 44.2 91.8\n", + "3 57.4 96.1 69.5\n", + "4 83.6 85.4 35.9\n", + "5 49.0 0.1 89.1\n", + "6 23.3 95.0 26.9\n", + "7 27.6 53.8 68.5\n", + "8 96.6 53.4 50.1\n", + "9 73.7 43.2 34.7" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[['Score_1', 'Score_3', 'Score_5']]" + ] }, { "cell_type": "markdown", @@ -161,10 +587,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "56.95000000000001" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avarage_score_3 = df[\"Score_3\"].mean()\n", + "avarage_score_3" + ] }, { "cell_type": "markdown", @@ -175,10 +615,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "88.8" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_score_4 = df[\"Score_4\"].max()\n", + "max_score_4" + ] }, { "cell_type": "markdown", @@ -189,10 +643,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "40.75" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_score_2 = df[\"Score_2\"].median()\n", + "median_score_2" + ] }, { "cell_type": "markdown", @@ -203,7 +671,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -224,10 +692,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "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": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = pd.DataFrame(orders)\n", + "df2" + ] }, { "cell_type": "markdown", @@ -238,10 +830,147 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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DescriptionQuantityUnitPriceRevenueSum
0LUNCH BAG APPLE DESIGN11.651.652.65
1SET OF 60 VINTAGE LEAF CAKE CASES240.5513.2037.20
2RIBBON REEL STRIPES DESIGN11.651.652.65
3WORLD WAR 2 GLIDERS ASSTD DESIGNS28800.18518.403398.40
4PLAYING CARDS JUBILEE UNION JACK21.252.504.50
5POPCORN HOLDER70.855.9512.95
6BOX OF VINTAGE ALPHABET BLOCKS111.9511.9512.95
7PARTY BUNTING44.9519.8023.80
8JAZZ HEARTS ADDRESS BOOK100.191.9011.90
9SET OF 4 SANTA PLACE SETTINGS481.2560.00108.00
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" + ], + "text/plain": [ + " Description Quantity UnitPrice Revenue Sum\n", + "0 LUNCH BAG APPLE DESIGN 1 1.65 1.65 2.65\n", + "1 SET OF 60 VINTAGE LEAF CAKE CASES 24 0.55 13.20 37.20\n", + "2 RIBBON REEL STRIPES DESIGN 1 1.65 1.65 2.65\n", + "3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 0.18 518.40 3398.40\n", + "4 PLAYING CARDS JUBILEE UNION JACK 2 1.25 2.50 4.50\n", + "5 POPCORN HOLDER 7 0.85 5.95 12.95\n", + "6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95 12.95\n", + "7 PARTY BUNTING 4 4.95 19.80 23.80\n", + "8 JAZZ HEARTS ADDRESS BOOK 10 0.19 1.90 11.90\n", + "9 SET OF 4 SANTA PLACE SETTINGS 48 1.25 60.00 108.00" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "col_list = ['Quantity', 'Revenue']\n", + "\n", + "df2['Sum'] = df2[col_list].sum(axis=1)\n", + "df2" + ] }, { "cell_type": "markdown", @@ -252,10 +981,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "the most expensive is: 11.95\n", + "the least expensive is: 0.18\n" + ] + } + ], + "source": [ + "most_expensive = df2[\"UnitPrice\"].max()\n", + "least_expensive = df2[\"UnitPrice\"].min()\n", + "print(\"the most expensive is: \", most_expensive)\n", + "print(\"the least expensive is: \", least_expensive)" + ] }, { "cell_type": "markdown", @@ -266,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -285,10 +1028,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "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
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": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.head()" + ] }, { "cell_type": "markdown", @@ -299,10 +1162,217 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0FalseFalseFalseFalseFalseFalseFalseFalseFalse
1FalseFalseFalseFalseFalseFalseFalseFalseFalse
2FalseFalseFalseFalseFalseFalseFalseFalseFalse
3FalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalse
..............................
380FalseFalseFalseFalseFalseFalseFalseFalseFalse
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382FalseFalseFalseFalseFalseFalseFalseFalseFalse
383FalseFalseFalseFalseFalseFalseFalseFalseFalse
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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", + "0 False False False False False False \n", + "1 False False False False False False \n", + "2 False False False False False False \n", + "3 False False False False False False \n", + "4 False False False False False False \n", + ".. ... ... ... ... ... ... \n", + "380 False False False False False False \n", + "381 False False False False False False \n", + "382 False False False False False False \n", + "383 False False False False False False \n", + "384 False False False False False False \n", + "\n", + " CGPA Research Chance of Admit \n", + "0 False False False \n", + "1 False False False \n", + "2 False False False \n", + "3 False False False \n", + "4 False False False \n", + ".. ... ... ... \n", + "380 False False False \n", + "381 False False False \n", + "382 False False False \n", + "383 False False False \n", + "384 False False False \n", + "\n", + "[385 rows x 9 columns]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.isnull()" + ] }, { "cell_type": "markdown", @@ -313,31 +1383,286 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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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
...........................
38132411033.53.59.0410.82
38232510733.03.59.1110.84
38333011645.04.59.4510.91
38431210333.54.08.7800.67
38533311745.04.09.6610.95
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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": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_index = admissions.set_index('Serial No.')\n", + "new_index" + ] + }, + { + "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": null, + "execution_count": 91, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "53" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions['CGPA'].duplicated().sum()" + ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 92, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "84" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\"Turns out that GRE Score and CGPA also uniquely identify the data. Show this in the cell below.\"" + "admissions['GRE Score'].duplicated().sum()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions[['GRE Score', 'CGPA']].duplicated().sum()" + ] }, { "cell_type": "markdown", @@ -348,10 +1673,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'CGPA' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[83], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(admissions[admissions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCGPA\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m9\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of rows in column \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mCGPA\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m greater than 9: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcount\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'CGPA' is not defined" + ] + } + ], + "source": [ + "count = len(admissions[admissions[\"CGPA\"] > 9])\n", + "\n", + "print(f\"Number of rows in column '{CGPA}' greater than 9: {count}\")" + ] }, { "cell_type": "markdown", @@ -449,7 +1790,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.11.5" }, "toc": { "base_numbering": "",