diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..028ef93 100644 --- a/your-code/pandas_1.ipynb +++ b/your-code/pandas_1.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 146, "metadata": {}, "outputs": [], "source": [ @@ -44,10 +44,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 147, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0 5.7\n", + "1 75.2\n", + "2 74.4\n", + "3 84.0\n", + "4 66.5\n", + "5 66.3\n", + "6 55.8\n", + "7 75.7\n", + "8 29.1\n", + "9 43.7\n", + "dtype: float64" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_lst = pd.Series(lst)\n", + "\n", + "new_lst" + ] }, { "cell_type": "markdown", @@ -60,10 +85,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "third_value = new_lst.iloc[2]\n", + "\n", + "third_value" + ] }, { "cell_type": "markdown", @@ -74,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 149, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +132,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " A B C D E\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": [ + "data_frame = pd.DataFrame(b, columns= ['A', 'B', 'C', 'D', 'E'])\n", + "\n", + "print(data_frame)" + ] }, { "cell_type": "markdown", @@ -106,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 151, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 152, "metadata": {}, "outputs": [], "source": [ @@ -133,10 +195,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 153, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Score_1Score_2Score_3Score_4Score_5
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161.340.830.837.887.6
220.673.244.214.691.8
357.40.196.14.269.5
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" + ], + "text/plain": [ + " Score_1 Score_2 Score_3 Score_4 Score_5\n", + "0 53.1 95.0 67.5 35.0 78.4\n", + "1 61.3 40.8 30.8 37.8 87.6\n", + "2 20.6 73.2 44.2 14.6 91.8\n", + "3 57.4 0.1 96.1 4.2 69.5\n", + "4 83.6 20.5 85.4 22.8 35.9\n", + "5 49.0 69.0 0.1 31.8 89.1\n", + "6 23.3 40.7 95.0 83.8 26.9\n", + "7 27.6 26.4 53.8 88.8 68.5\n", + "8 96.6 96.4 53.4 72.4 50.1\n", + "9 73.7 39.0 43.2 81.6 34.7" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data_frame = pd.DataFrame(b, columns =['Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5'])\n", + "\n", + "new_data_frame" + ] }, { "cell_type": "markdown", @@ -147,10 +345,81 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 154, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Score_1Score_2Score_3Score_4Score_5
161.340.830.837.887.6
357.40.196.14.269.5
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" + ], + "text/plain": [ + " Score_1 Score_2 Score_3 Score_4 Score_5\n", + "1 61.3 40.8 30.8 37.8 87.6\n", + "3 57.4 0.1 96.1 4.2 69.5\n", + "5 49.0 69.0 0.1 31.8 89.1" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_data_frame.loc[[1,3,5]]" + ] }, { "cell_type": "markdown", @@ -161,10 +430,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 155, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average score in Score_3 column is 56.95000000000001\n" + ] + } + ], + "source": [ + "mean_3 = new_data_frame['Score_3'].mean()\n", + "\n", + "print (\"The average score in Score_3 column is\", mean_3)" + ] }, { "cell_type": "markdown", @@ -175,10 +456,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 156, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The maximum in Score_4 column is 88.8\n" + ] + } + ], + "source": [ + "max_4 = new_data_frame['Score_4'].max()\n", + "\n", + "print (\"The maximum in Score_4 column is\", max_4)" + ] }, { "cell_type": "markdown", @@ -189,10 +482,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 157, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The median in Score_2 column is 40.75\n" + ] + } + ], + "source": [ + "median_2 = new_data_frame['Score_2'].median()\n", + "\n", + "print (\"The median in Score_2 column is\", median_2)" + ] }, { "cell_type": "markdown", @@ -203,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 158, "metadata": {}, "outputs": [], "source": [ @@ -224,10 +529,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 159, "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": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(orders)\n", + "df" + ] }, { "cell_type": "markdown", @@ -238,10 +667,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 160, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total quantity ordered is 2978\n", + "The total revenue is 637.0\n" + ] + } + ], + "source": [ + "sum_quantity = df['Quantity'].sum()\n", + "sum_revenue = df['Revenue'].sum()\n", + "\n", + "print (\"The total quantity ordered is\", sum_quantity)\n", + "\n", + "print (\"The total revenue is\", sum_revenue)\n", + "\n" + ] }, { "cell_type": "markdown", @@ -252,10 +698,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 161, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Most Expensive Product:\n", + " Description Quantity UnitPrice Revenue\n", + "6 BOX OF VINTAGE ALPHABET BLOCKS 1 11.95 11.95\n", + "\n", + "Least Expensive Product:\n", + " Description Quantity UnitPrice Revenue\n", + "3 WORLD WAR 2 GLIDERS ASSTD DESIGNS 2880 0.18 518.4\n" + ] + } + ], + "source": [ + "most_expensive_product = df[df['UnitPrice'] == df['UnitPrice'].max()]\n", + "\n", + "least_expensive_product = df[df['UnitPrice'] == df['UnitPrice'].min()]\n", + "\n", + "print(\"Most Expensive Product:\")\n", + "print(most_expensive_product)\n", + "\n", + "print(\"\\nLeast Expensive Product:\")\n", + "print(least_expensive_product)\n" + ] }, { "cell_type": "markdown", @@ -266,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 162, "metadata": {}, "outputs": [], "source": [ @@ -285,10 +755,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 163, "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
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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": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.head()\n", + "\n" + ] }, { "cell_type": "markdown", @@ -299,10 +890,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 164, "metadata": {}, - "outputs": [], - "source": [] + "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": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.isnull().sum()" + ] }, { "cell_type": "markdown", @@ -313,17 +926,358 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 165, "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
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
Serial No.
1133711844.54.59.6510.92
2231610433.03.58.0010.72
3332211033.52.58.6710.80
4431410322.03.08.2100.65
5533011554.53.09.3410.90
..............................
38138132411033.53.59.0410.82
38238232510733.03.59.1110.84
38338333011645.04.59.4510.91
38438431210333.54.08.7800.67
38538533311745.04.09.6610.95
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385 rows × 9 columns

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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitSerial No.2GRE - CGPA
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