diff --git a/your-code/pandas_1.ipynb b/your-code/pandas_1.ipynb index 4f428ac..e625c22 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": 3, "metadata": {}, "outputs": [], "source": [ @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -44,10 +44,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "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": 10, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "74.4" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series_lst.values[2]" + ] }, { "cell_type": "markdown", @@ -74,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -92,10 +126,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "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", @@ -106,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -133,10 +188,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "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_b1 = df_b.rename(columns = {0: 'Score_1', 1: 'Score_2', 2: 'Score_3', 3: 'Score_4', 4: 'Score_5'})\n", + "print(df_b1)" + ] }, { "cell_type": "markdown", @@ -147,10 +223,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "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": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_b1[['Score_1', 'Score_3', 'Score_5']]" + ] }, { "cell_type": "markdown", @@ -161,10 +349,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "2 56.95\n", + "dtype: float64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_b_score3 = df_b[[2]]\n", + "\n", + "df_b_score3.mean()" + ] }, { "cell_type": "markdown", @@ -175,10 +379,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "3 88.8\n", + "dtype: float64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_b_score4 = df_b[[3]]\n", + "df_b_score4.max()" + ] }, { "cell_type": "markdown", @@ -189,10 +408,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "1 40.75\n", + "dtype: float64" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_b_score2 = df_b[[1]]\n", + "df_b_score2.median()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 40.75\n", + "dtype: float64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_b[[1]].median()" + ] }, { "cell_type": "markdown", @@ -203,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -224,10 +479,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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\n" + ] + } + ], + "source": [ + "df_orders = pd.DataFrame(orders)\n", + "print(df_orders)" + ] }, { "cell_type": "markdown", @@ -238,10 +514,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Quantity 2978\n", + "dtype: int64\n", + "Revenue 637.0\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "total_quantity = df_orders[list({'Quantity'})].sum()\n", + "print(total_quantity)\n", + "\n", + "total_revenue = df_orders[list({'Revenue'})].sum()\n", + "print(total_revenue)" + ] }, { "cell_type": "markdown", @@ -252,10 +545,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "UnitPrice 11.95\n", + "dtype: float64\n", + "UnitPrice 0.18\n", + "dtype: float64\n", + "UnitPrice 11.77\n", + "dtype: float64\n" + ] + } + ], + "source": [ + "highest_price = df_orders[list({'UnitPrice'})].max()\n", + "print(highest_price)\n", + "\n", + "lowest_price = df_orders[list({'UnitPrice'})].min()\n", + "print(lowest_price)\n", + "\n", + "print(highest_price - lowest_price)" + ] }, { "cell_type": "markdown", @@ -266,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -285,10 +599,130 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1231610433.03.58.0010.72
2332211033.52.58.6710.80
3431410322.03.08.2100.65
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" + ], + "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": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.head()" + ] }, { "cell_type": "markdown", @@ -299,10 +733,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "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": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "admissions.isnull().sum()" + ] }, { "cell_type": "markdown", @@ -313,10 +769,219 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "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
...........................
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GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitGRE_CGPA
Serial No.
133711844.54.59.65True0.92337-9.65
533011554.53.09.34True0.90330-9.34
1132811244.04.59.10True0.78328-9.1
2032811655.05.09.50True0.94328-9.5
2133411955.04.59.70True0.95334-9.7
..............................
38032911144.54.09.23True0.89329-9.23
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38232510733.03.59.11True0.84325-9.11
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38533311745.04.09.66True0.95333-9.66
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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", + "5 330 115 5 4.5 3.0 9.34 \n", + "11 328 112 4 4.0 4.5 9.10 \n", + "20 328 116 5 5.0 5.0 9.50 \n", + "21 334 119 5 5.0 4.5 9.70 \n", + "... ... ... ... ... ... ... \n", + "380 329 111 4 4.5 4.0 9.23 \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", + "385 333 117 4 5.0 4.0 9.66 \n", + "\n", + " Research Chance of Admit GRE_CGPA \n", + "Serial No. \n", + "1 True 0.92 337-9.65 \n", + "5 True 0.90 330-9.34 \n", + "11 True 0.78 328-9.1 \n", + "20 True 0.94 328-9.5 \n", + "21 True 0.95 334-9.7 \n", + "... ... ... ... \n", + "380 True 0.89 329-9.23 \n", + "381 True 0.82 324-9.04 \n", + "382 True 0.84 325-9.11 \n", + "383 True 0.91 330-9.45 \n", + "385 True 0.95 333-9.66 \n", + "\n", + "[110 rows x 9 columns]" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "condition_1 = (admissions[\"CGPA\"] > 9) & (admissions[\"Research\"] == 1)\n", + "admissions_filter1 = admissions[condition_1]\n", + "admissions_filter1\n", + "##not sure why this wasn't working when I tried to do it in 2 separate conditions" + ] }, { "cell_type": "markdown", @@ -362,17 +1265,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitGRE_CGPA
Serial No.
2933811843.04.59.40True0.91338-9.4
6332711433.03.09.02True0.61327-9.02
14132611433.03.09.11True0.83326-9.11
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" + ], + "text/plain": [ + " GRE Score TOEFL Score University Rating SOP LOR CGPA \\\n", + "Serial No. \n", + "29 338 118 4 3.0 4.5 9.40 \n", + "63 327 114 3 3.0 3.0 9.02 \n", + "141 326 114 3 3.0 3.0 9.11 \n", + "218 324 111 4 3.0 3.0 9.01 \n", + "382 325 107 3 3.0 3.5 9.11 \n", + "\n", + " Research Chance of Admit GRE_CGPA \n", + "Serial No. \n", + "29 True 0.91 338-9.4 \n", + "63 True 0.61 327-9.02 \n", + "141 True 0.83 326-9.11 \n", + "218 True 0.82 324-9.01 \n", + "382 True 0.84 325-9.11 " + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "condition_2 = (admissions[\"CGPA\"] > 9) & (admissions[\"SOP\"] < 3.5)\n", + "admissions_filter2 = admissions[condition_2]\n", + "admissions_filter2" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "0.8019999999999999" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_chance = admissions_filter2[\"Chance of Admit\"].mean()\n", + "mean_chance" + ] }, { "cell_type": "markdown", @@ -384,10 +1437,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def toefl_condition(score):\n", + " return score > 100" + ] }, { "cell_type": "markdown", @@ -398,31 +1454,515 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of AdmitGRE_CGPADecisiondecision2
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332211033.52.58.67True0.80322-8.67True1
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