├── .gitattributes ├── .ipynb_checkpoints ├── Lecture 10 - Array Transposition-checkpoint.ipynb ├── Lecture 11 - Universal Array Functions-checkpoint.ipynb ├── Lecture 12 - Array Processing-checkpoint.ipynb ├── Lecture 13 - Array Input and Output-checkpoint.ipynb ├── Lecture 14 - Series-checkpoint.ipynb ├── Lecture 15 - DataFrame-checkpoint.ipynb ├── Lecture 16 - Index Objects-checkpoint.ipynb ├── Lecture 17 - Reindex-checkpoint.ipynb ├── Lecture 18 - Drop Entry-checkpoint.ipynb ├── Lecture 19 - Selecting Entries-checkpoint.ipynb ├── Lecture 20 - Data Alignment-checkpoint.ipynb ├── Lecture 21 - Rank and Sort-checkpoint.ipynb ├── Lecture 22 - Summary Statistics-checkpoint.ipynb ├── Lecture 23 - Missing Data-checkpoint.ipynb ├── Lecture 24 - Index Hierarchy-checkpoint.ipynb ├── Lecture 25 - Reading and Writing Text Files-checkpoint.ipynb ├── Lecture 26 - JSON with Python-checkpoint.ipynb ├── Lecture 27 - HTML with Python-checkpoint.ipynb ├── Lecture 28 - Excel with Python-checkpoint.ipynb ├── Lecture 29 - Merge-checkpoint.ipynb ├── Lecture 30 - Merge on Index-checkpoint.ipynb ├── Lecture 31 - Concatenate-checkpoint.ipynb ├── Lecture 32 - Combining DataFrames-checkpoint.ipynb ├── Lecture 33 - Reshaping-checkpoint.ipynb ├── Lecture 34 - Pivoting-checkpoint.ipynb ├── Lecture 35 - Duplicates in DataFrames-checkpoint.ipynb ├── Lecture 36 - Mapping-checkpoint.ipynb ├── Lecture 37 - Replace-checkpoint.ipynb ├── Lecture 38 - Rename Index-checkpoint.ipynb ├── Lecture 39 - Binning-checkpoint.ipynb ├── Lecture 40 - Outliers-checkpoint.ipynb ├── Lecture 41 - Permutation-checkpoint.ipynb ├── Lecture 42 - Groupby on DataFrames-checkpoint.ipynb ├── Lecture 43 - GroupBy on Dict and Series-checkpoint.ipynb ├── Lecture 44 - Aggregation-checkpoint.ipynb ├── Lecture 45 - Split Applying and Combining-checkpoint.ipynb ├── Lecture 46 - Cross Tabulation-checkpoint.ipynb ├── Lecture 47 - Installing Seaborn-checkpoint.ipynb ├── Lecture 48 - Histograms-checkpoint.ipynb ├── Lecture 49 - Kernel Density Estimation Plots-checkpoint.ipynb ├── Lecture 50 = Combining Plot Styles-checkpoint.ipynb ├── Lecture 51 - Box and Violin Plots-checkpoint.ipynb ├── Lecture 52 - Regression Plots-checkpoint.ipynb ├── Lecture 53 - Heatmaps and Clustered Matrices-checkpoint.ipynb ├── Lecture 7 - Creating arrays-checkpoint.ipynb ├── Lecture 8 - Using arrays and scalars-checkpoint.ipynb ├── Lecture 9 - Indexing Arrays-checkpoint.ipynb ├── Python Overview Part 1-checkpoint.ipynb ├── Python Overview Part 2-checkpoint.ipynb └── Python Overview Part 3-checkpoint.ipynb ├── Lec_28_test.xlsx ├── Lecture 10 - Array Transposition.ipynb ├── Lecture 11 - Universal Array Functions.ipynb ├── Lecture 12 - Array Processing.ipynb ├── Lecture 13 - Array Input and Output.ipynb ├── Lecture 14 - Series.ipynb ├── Lecture 15 - DataFrame.ipynb ├── Lecture 16 - Index Objects.ipynb ├── Lecture 17 - Reindex.ipynb ├── Lecture 18 - Drop Entry.ipynb ├── Lecture 19 - Selecting Entries.ipynb ├── Lecture 20 - Data Alignment.ipynb ├── Lecture 21 - Rank and Sort.ipynb ├── Lecture 22 - Summary Statistics.ipynb ├── Lecture 23 - Missing Data.ipynb ├── Lecture 24 - Index Hierarchy.ipynb ├── Lecture 25 - Reading and Writing Text Files.ipynb ├── Lecture 26 - JSON with Python.ipynb ├── Lecture 27 - HTML with Python.ipynb ├── Lecture 28 - Excel with Python.ipynb ├── Lecture 29 - Merge.ipynb ├── Lecture 30 - Merge on Index.ipynb ├── Lecture 31 - Concatenate.ipynb ├── Lecture 32 - Combining DataFrames.ipynb ├── Lecture 33 - Reshaping.ipynb ├── Lecture 34 - Pivoting.ipynb ├── Lecture 35 - Duplicates in DataFrames.ipynb ├── Lecture 36 - Mapping.ipynb ├── Lecture 37 - Replace.ipynb ├── Lecture 38 - Rename Index.ipynb ├── Lecture 39 - Binning.ipynb ├── Lecture 40 - Outliers.ipynb ├── Lecture 41 - Permutation.ipynb ├── Lecture 42 - Groupby on DataFrames.ipynb ├── Lecture 43 - GroupBy on Dict and Series.ipynb ├── Lecture 44 - Aggregation.ipynb ├── Lecture 45 - Split Applying and Combining.ipynb ├── Lecture 46 - Cross Tabulation.ipynb ├── Lecture 47 - Installing Seaborn.ipynb ├── Lecture 48 - Histograms.ipynb ├── Lecture 49 - Kernel Density Estimation Plots.ipynb ├── Lecture 50 = Combining Plot Styles.ipynb ├── Lecture 51 - Box and Violin Plots.ipynb ├── Lecture 52 - Regression Plots.ipynb ├── Lecture 53 - Heatmaps and Clustered Matrices.ipynb ├── Lecture 7 - Creating arrays.ipynb ├── Lecture 8 - Using arrays and scalars.ipynb ├── Lecture 9 - Indexing Arrays.ipynb ├── Projects ├── Stock Market Analysis │ ├── .ipynb_checkpoints │ │ └── Stock Market Analysis-checkpoint.ipynb │ └── Stock Market Analysis.ipynb └── Titanic Project │ ├── .ipynb_checkpoints │ └── Titanic Intro Project-checkpoint.ipynb │ └── Titanic Intro Project.ipynb ├── Python Overview Part 1.ipynb ├── Python Overview Part 2.ipynb ├── Python Overview Part 3.ipynb ├── lec25.csv ├── myarray.npy ├── mytextarray.txt ├── mytextdata_out.csv ├── winequality_red.csv └── ziparray.npz /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 10 - Array Transposition-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 4, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "array([[ 0, 1, 2, 3, 4],\n", 25 | " [ 5, 6, 7, 8, 9],\n", 26 | " [10, 11, 12, 13, 14],\n", 27 | " [15, 16, 17, 18, 19],\n", 28 | " [20, 21, 22, 23, 24],\n", 29 | " [25, 26, 27, 28, 29],\n", 30 | " [30, 31, 32, 33, 34],\n", 31 | " [35, 36, 37, 38, 39],\n", 32 | " [40, 41, 42, 43, 44],\n", 33 | " [45, 46, 47, 48, 49]])" 34 | ] 35 | }, 36 | "execution_count": 4, 37 | "metadata": {}, 38 | "output_type": "execute_result" 39 | } 40 | ], 41 | "source": [ 42 | "arr = np.arange(50).reshape((10,5))\n", 43 | "arr" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 5, 49 | "metadata": { 50 | "collapsed": false 51 | }, 52 | "outputs": [ 53 | { 54 | "data": { 55 | "text/plain": [ 56 | "array([[ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45],\n", 57 | " [ 1, 6, 11, 16, 21, 26, 31, 36, 41, 46],\n", 58 | " [ 2, 7, 12, 17, 22, 27, 32, 37, 42, 47],\n", 59 | " [ 3, 8, 13, 18, 23, 28, 33, 38, 43, 48],\n", 60 | " [ 4, 9, 14, 19, 24, 29, 34, 39, 44, 49]])" 61 | ] 62 | }, 63 | "execution_count": 5, 64 | "metadata": {}, 65 | "output_type": "execute_result" 66 | } 67 | ], 68 | "source": [ 69 | "arr.T" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 6, 75 | "metadata": { 76 | "collapsed": false 77 | }, 78 | "outputs": [ 79 | { 80 | "data": { 81 | "text/plain": [ 82 | "array([[7125, 7350, 7575, 7800, 8025],\n", 83 | " [7350, 7585, 7820, 8055, 8290],\n", 84 | " [7575, 7820, 8065, 8310, 8555],\n", 85 | " [7800, 8055, 8310, 8565, 8820],\n", 86 | " [8025, 8290, 8555, 8820, 9085]])" 87 | ] 88 | }, 89 | "execution_count": 6, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | } 93 | ], 94 | "source": [ 95 | "np.dot(arr.T, arr)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 7, 101 | "metadata": { 102 | "collapsed": false 103 | }, 104 | "outputs": [ 105 | { 106 | "data": { 107 | "text/plain": [ 108 | "array([[[ 0, 1],\n", 109 | " [ 2, 3],\n", 110 | " [ 4, 5],\n", 111 | " [ 6, 7],\n", 112 | " [ 8, 9]],\n", 113 | "\n", 114 | " [[10, 11],\n", 115 | " [12, 13],\n", 116 | " [14, 15],\n", 117 | " [16, 17],\n", 118 | " [18, 19]],\n", 119 | "\n", 120 | " [[20, 21],\n", 121 | " [22, 23],\n", 122 | " [24, 25],\n", 123 | " [26, 27],\n", 124 | " [28, 29]],\n", 125 | "\n", 126 | " [[30, 31],\n", 127 | " [32, 33],\n", 128 | " [34, 35],\n", 129 | " [36, 37],\n", 130 | " [38, 39]],\n", 131 | "\n", 132 | " [[40, 41],\n", 133 | " [42, 43],\n", 134 | " [44, 45],\n", 135 | " [46, 47],\n", 136 | " [48, 49]]])" 137 | ] 138 | }, 139 | "execution_count": 7, 140 | "metadata": {}, 141 | "output_type": "execute_result" 142 | } 143 | ], 144 | "source": [ 145 | "arr3d = np.arange(50).reshape((5,5,2))\n", 146 | "arr3d" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 10, 152 | "metadata": { 153 | "collapsed": false 154 | }, 155 | "outputs": [ 156 | { 157 | "data": { 158 | "text/plain": [ 159 | "array([[[ 0, 1],\n", 160 | " [ 2, 3],\n", 161 | " [ 4, 5],\n", 162 | " [ 6, 7],\n", 163 | " [ 8, 9]],\n", 164 | "\n", 165 | " [[10, 11],\n", 166 | " [12, 13],\n", 167 | " [14, 15],\n", 168 | " [16, 17],\n", 169 | " [18, 19]],\n", 170 | "\n", 171 | " [[20, 21],\n", 172 | " [22, 23],\n", 173 | " [24, 25],\n", 174 | " [26, 27],\n", 175 | " [28, 29]],\n", 176 | "\n", 177 | " [[30, 31],\n", 178 | " [32, 33],\n", 179 | " [34, 35],\n", 180 | " [36, 37],\n", 181 | " [38, 39]],\n", 182 | "\n", 183 | " [[40, 41],\n", 184 | " [42, 43],\n", 185 | " [44, 45],\n", 186 | " [46, 47],\n", 187 | " [48, 49]]])" 188 | ] 189 | }, 190 | "execution_count": 10, 191 | "metadata": {}, 192 | "output_type": "execute_result" 193 | } 194 | ], 195 | "source": [ 196 | "arr3d.transpose((1,0,2))" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 11, 202 | "metadata": { 203 | "collapsed": false 204 | }, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "text/plain": [ 209 | "array([[1, 2, 3]])" 210 | ] 211 | }, 212 | "execution_count": 11, 213 | "metadata": {}, 214 | "output_type": "execute_result" 215 | } 216 | ], 217 | "source": [ 218 | "arr = np.array([[1,2,3]])\n", 219 | "arr" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": 13, 225 | "metadata": { 226 | "collapsed": false 227 | }, 228 | "outputs": [ 229 | { 230 | "data": { 231 | "text/plain": [ 232 | "array([[1],\n", 233 | " [2],\n", 234 | " [3]])" 235 | ] 236 | }, 237 | "execution_count": 13, 238 | "metadata": {}, 239 | "output_type": "execute_result" 240 | } 241 | ], 242 | "source": [ 243 | "arr.swapaxes(0,1)" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": null, 249 | "metadata": { 250 | "collapsed": true 251 | }, 252 | "outputs": [], 253 | "source": [] 254 | } 255 | ], 256 | "metadata": { 257 | "kernelspec": { 258 | "display_name": "Python 3", 259 | "language": "python", 260 | "name": "python3" 261 | }, 262 | "language_info": { 263 | "codemirror_mode": { 264 | "name": "ipython", 265 | "version": 3 266 | }, 267 | "file_extension": ".py", 268 | "mimetype": "text/x-python", 269 | "name": "python", 270 | "nbconvert_exporter": "python", 271 | "pygments_lexer": "ipython3", 272 | "version": "3.5.0" 273 | } 274 | }, 275 | "nbformat": 4, 276 | "nbformat_minor": 0 277 | } 278 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 11 - Universal Array Functions-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" 25 | ] 26 | }, 27 | "execution_count": 2, 28 | "metadata": {}, 29 | "output_type": "execute_result" 30 | } 31 | ], 32 | "source": [ 33 | "arr = np.arange(11)\n", 34 | "arr" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 3, 40 | "metadata": { 41 | "collapsed": false 42 | }, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "array([ 0. , 1. , 1.41421356, 1.73205081, 2. ,\n", 48 | " 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ,\n", 49 | " 3.16227766])" 50 | ] 51 | }, 52 | "execution_count": 3, 53 | "metadata": {}, 54 | "output_type": "execute_result" 55 | } 56 | ], 57 | "source": [ 58 | "np.sqrt(arr)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 4, 64 | "metadata": { 65 | "collapsed": false 66 | }, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "text/plain": [ 71 | "array([ 1.00000000e+00, 2.71828183e+00, 7.38905610e+00,\n", 72 | " 2.00855369e+01, 5.45981500e+01, 1.48413159e+02,\n", 73 | " 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n", 74 | " 8.10308393e+03, 2.20264658e+04])" 75 | ] 76 | }, 77 | "execution_count": 4, 78 | "metadata": {}, 79 | "output_type": "execute_result" 80 | } 81 | ], 82 | "source": [ 83 | "np.exp(arr)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 5, 89 | "metadata": { 90 | "collapsed": false 91 | }, 92 | "outputs": [ 93 | { 94 | "data": { 95 | "text/plain": [ 96 | "array([ 0.56041841, 0.39410462, -1.40311281, 0.52177286, -0.88397746,\n", 97 | " 0.50731782, 1.48511633, -0.94075355, -0.22938291, 0.04752728])" 98 | ] 99 | }, 100 | "execution_count": 5, 101 | "metadata": {}, 102 | "output_type": "execute_result" 103 | } 104 | ], 105 | "source": [ 106 | "A = np.random.randn(10)\n", 107 | "A" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 6, 113 | "metadata": { 114 | "collapsed": false 115 | }, 116 | "outputs": [ 117 | { 118 | "data": { 119 | "text/plain": [ 120 | "array([-0.2517604 , 1.90173753, -1.13185127, 0.73174884, 0.46465893,\n", 121 | " -0.38132952, -0.11073532, 1.44856624, 0.92181173, 1.31479299])" 122 | ] 123 | }, 124 | "execution_count": 6, 125 | "metadata": {}, 126 | "output_type": "execute_result" 127 | } 128 | ], 129 | "source": [ 130 | "B = np.random.randn(10)\n", 131 | "B" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 8, 137 | "metadata": { 138 | "collapsed": false 139 | }, 140 | "outputs": [ 141 | { 142 | "data": { 143 | "text/plain": [ 144 | "array([ 0.30865802, 2.29584215, -2.53496407, 1.2535217 , -0.41931853,\n", 145 | " 0.1259883 , 1.37438101, 0.5078127 , 0.69242882, 1.36232026])" 146 | ] 147 | }, 148 | "execution_count": 8, 149 | "metadata": {}, 150 | "output_type": "execute_result" 151 | } 152 | ], 153 | "source": [ 154 | "#Binary Functions\n", 155 | "np.add(A,B)" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 9, 161 | "metadata": { 162 | "collapsed": false 163 | }, 164 | "outputs": [ 165 | { 166 | "data": { 167 | "text/plain": [ 168 | "array([ 0.56041841, 1.90173753, -1.13185127, 0.73174884, 0.46465893,\n", 169 | " 0.50731782, 1.48511633, 1.44856624, 0.92181173, 1.31479299])" 170 | ] 171 | }, 172 | "execution_count": 9, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "np.maximum(A,B)" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 10, 184 | "metadata": { 185 | "collapsed": false 186 | }, 187 | "outputs": [ 188 | { 189 | "data": { 190 | "text/plain": [ 191 | "True" 192 | ] 193 | }, 194 | "execution_count": 10, 195 | "metadata": {}, 196 | "output_type": "execute_result" 197 | } 198 | ], 199 | "source": [ 200 | "#Universal Functions on Arrays\n", 201 | "website = \"http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs\"\n", 202 | "import webbrowser\n", 203 | "webbrowser.open(website)" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": true 211 | }, 212 | "outputs": [], 213 | "source": [] 214 | } 215 | ], 216 | "metadata": { 217 | "kernelspec": { 218 | "display_name": "Python 3", 219 | "language": "python", 220 | "name": "python3" 221 | }, 222 | "language_info": { 223 | "codemirror_mode": { 224 | "name": "ipython", 225 | "version": 3 226 | }, 227 | "file_extension": ".py", 228 | "mimetype": "text/x-python", 229 | "name": "python", 230 | "nbconvert_exporter": "python", 231 | "pygments_lexer": "ipython3", 232 | "version": "3.5.0" 233 | } 234 | }, 235 | "nbformat": 4, 236 | "nbformat_minor": 0 237 | } 238 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 13 - Array Input and Output-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "arr = np.arange(5)" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "collapsed": false 30 | }, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "array([0, 1, 2, 3, 4])" 36 | ] 37 | }, 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "output_type": "execute_result" 41 | } 42 | ], 43 | "source": [ 44 | "arr" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 5, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "array([0, 1, 2, 3, 4])" 58 | ] 59 | }, 60 | "execution_count": 5, 61 | "metadata": {}, 62 | "output_type": "execute_result" 63 | } 64 | ], 65 | "source": [ 66 | "np.save('myarray',arr)\n", 67 | "arr" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 6, 73 | "metadata": { 74 | "collapsed": false 75 | }, 76 | "outputs": [ 77 | { 78 | "data": { 79 | "text/plain": [ 80 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 81 | ] 82 | }, 83 | "execution_count": 6, 84 | "metadata": {}, 85 | "output_type": "execute_result" 86 | } 87 | ], 88 | "source": [ 89 | "arr = np.arange(10)\n", 90 | "arr" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 7, 96 | "metadata": { 97 | "collapsed": false 98 | }, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "text/plain": [ 103 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 104 | ] 105 | }, 106 | "execution_count": 7, 107 | "metadata": {}, 108 | "output_type": "execute_result" 109 | } 110 | ], 111 | "source": [ 112 | "arr" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 8, 118 | "metadata": { 119 | "collapsed": false 120 | }, 121 | "outputs": [ 122 | { 123 | "data": { 124 | "text/plain": [ 125 | "array([0, 1, 2, 3, 4])" 126 | ] 127 | }, 128 | "execution_count": 8, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "np.load('myarray.npy')" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 11, 140 | "metadata": { 141 | "collapsed": false 142 | }, 143 | "outputs": [ 144 | { 145 | "data": { 146 | "text/plain": [ 147 | "array([0, 1, 2, 3, 4])" 148 | ] 149 | }, 150 | "execution_count": 11, 151 | "metadata": {}, 152 | "output_type": "execute_result" 153 | } 154 | ], 155 | "source": [ 156 | "arr1 = np.load('myarray.npy')\n", 157 | "arr1" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 12, 163 | "metadata": { 164 | "collapsed": true 165 | }, 166 | "outputs": [], 167 | "source": [ 168 | "arr2 = arr" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 13, 174 | "metadata": { 175 | "collapsed": false 176 | }, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "text/plain": [ 181 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 182 | ] 183 | }, 184 | "execution_count": 13, 185 | "metadata": {}, 186 | "output_type": "execute_result" 187 | } 188 | ], 189 | "source": [ 190 | "arr2" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 14, 196 | "metadata": { 197 | "collapsed": true 198 | }, 199 | "outputs": [], 200 | "source": [ 201 | "np.savez('ziparray.npz',x=arr1,y=arr2)" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 15, 207 | "metadata": { 208 | "collapsed": true 209 | }, 210 | "outputs": [], 211 | "source": [ 212 | "archive_array = np.load('ziparray.npz')" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 16, 218 | "metadata": { 219 | "collapsed": false 220 | }, 221 | "outputs": [ 222 | { 223 | "data": { 224 | "text/plain": [ 225 | "array([0, 1, 2, 3, 4])" 226 | ] 227 | }, 228 | "execution_count": 16, 229 | "metadata": {}, 230 | "output_type": "execute_result" 231 | } 232 | ], 233 | "source": [ 234 | "archive_array['x']" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 17, 240 | "metadata": { 241 | "collapsed": false 242 | }, 243 | "outputs": [ 244 | { 245 | "data": { 246 | "text/plain": [ 247 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 248 | ] 249 | }, 250 | "execution_count": 17, 251 | "metadata": {}, 252 | "output_type": "execute_result" 253 | } 254 | ], 255 | "source": [ 256 | "archive_array['y']" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": 19, 262 | "metadata": { 263 | "collapsed": false 264 | }, 265 | "outputs": [ 266 | { 267 | "data": { 268 | "text/plain": [ 269 | "array([[1, 2, 3],\n", 270 | " [4, 5, 6]])" 271 | ] 272 | }, 273 | "execution_count": 19, 274 | "metadata": {}, 275 | "output_type": "execute_result" 276 | } 277 | ], 278 | "source": [ 279 | "arr = np.array([[1,2,3],[4,5,6]])\n", 280 | "arr" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": 22, 286 | "metadata": { 287 | "collapsed": false 288 | }, 289 | "outputs": [], 290 | "source": [ 291 | "np.savetxt('mytextarray.txt',arr,delimiter=',')" 292 | ] 293 | }, 294 | { 295 | "cell_type": "code", 296 | "execution_count": 24, 297 | "metadata": { 298 | "collapsed": false 299 | }, 300 | "outputs": [ 301 | { 302 | "data": { 303 | "text/plain": [ 304 | "array([[ 1., 2., 3.],\n", 305 | " [ 4., 5., 6.]])" 306 | ] 307 | }, 308 | "execution_count": 24, 309 | "metadata": {}, 310 | "output_type": "execute_result" 311 | } 312 | ], 313 | "source": [ 314 | "arr = np.loadtxt('mytextarray.txt',delimiter=',')\n", 315 | "arr" 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": null, 321 | "metadata": { 322 | "collapsed": true 323 | }, 324 | "outputs": [], 325 | "source": [] 326 | } 327 | ], 328 | "metadata": { 329 | "kernelspec": { 330 | "display_name": "Python 3", 331 | "language": "python", 332 | "name": "python3" 333 | }, 334 | "language_info": { 335 | "codemirror_mode": { 336 | "name": "ipython", 337 | "version": 3 338 | }, 339 | "file_extension": ".py", 340 | "mimetype": "text/x-python", 341 | "name": "python", 342 | "nbconvert_exporter": "python", 343 | "pygments_lexer": "ipython3", 344 | "version": "3.5.0" 345 | } 346 | }, 347 | "nbformat": 4, 348 | "nbformat_minor": 0 349 | } 350 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 16 - Index Objects-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "from pandas import Series, DataFrame\n", 13 | "import pandas as pd" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/plain": [ 26 | "A 1\n", 27 | "B 2\n", 28 | "C 3\n", 29 | "D 4\n", 30 | "dtype: int64" 31 | ] 32 | }, 33 | "execution_count": 3, 34 | "metadata": {}, 35 | "output_type": "execute_result" 36 | } 37 | ], 38 | "source": [ 39 | "my_ser = Series([1,2,3,4],index=['A','B','C','D'])\n", 40 | "my_ser" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 4, 46 | "metadata": { 47 | "collapsed": true 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "my_index = my_ser.index" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 5, 57 | "metadata": { 58 | "collapsed": false 59 | }, 60 | "outputs": [ 61 | { 62 | "data": { 63 | "text/plain": [ 64 | "Index(['A', 'B', 'C', 'D'], dtype='object')" 65 | ] 66 | }, 67 | "execution_count": 5, 68 | "metadata": {}, 69 | "output_type": "execute_result" 70 | } 71 | ], 72 | "source": [ 73 | "my_index" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 6, 79 | "metadata": { 80 | "collapsed": false 81 | }, 82 | "outputs": [ 83 | { 84 | "data": { 85 | "text/plain": [ 86 | "'C'" 87 | ] 88 | }, 89 | "execution_count": 6, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | } 93 | ], 94 | "source": [ 95 | "my_index[2]" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 8, 101 | "metadata": { 102 | "collapsed": false 103 | }, 104 | "outputs": [ 105 | { 106 | "data": { 107 | "text/plain": [ 108 | "Index(['C', 'D'], dtype='object')" 109 | ] 110 | }, 111 | "execution_count": 8, 112 | "metadata": {}, 113 | "output_type": "execute_result" 114 | } 115 | ], 116 | "source": [ 117 | "my_index[2:]" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 9, 123 | "metadata": { 124 | "collapsed": false 125 | }, 126 | "outputs": [ 127 | { 128 | "data": { 129 | "text/plain": [ 130 | "'A'" 131 | ] 132 | }, 133 | "execution_count": 9, 134 | "metadata": {}, 135 | "output_type": "execute_result" 136 | } 137 | ], 138 | "source": [ 139 | "my_index[0]" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": 10, 145 | "metadata": { 146 | "collapsed": false 147 | }, 148 | "outputs": [ 149 | { 150 | "ename": "TypeError", 151 | "evalue": "Index does not support mutable operations", 152 | "output_type": "error", 153 | "traceback": [ 154 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 155 | "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", 156 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_index\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 157 | "\u001b[1;32mD:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 1122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1123\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1124\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Index does not support mutable operations\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1125\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 158 | "\u001b[1;31mTypeError\u001b[0m: Index does not support mutable operations" 159 | ] 160 | } 161 | ], 162 | "source": [ 163 | "my_index[0] = 'Z' #You cannot change them. They are const." 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "metadata": { 170 | "collapsed": true 171 | }, 172 | "outputs": [], 173 | "source": [] 174 | } 175 | ], 176 | "metadata": { 177 | "kernelspec": { 178 | "display_name": "Python 3", 179 | "language": "python", 180 | "name": "python3" 181 | }, 182 | "language_info": { 183 | "codemirror_mode": { 184 | "name": "ipython", 185 | "version": 3 186 | }, 187 | "file_extension": ".py", 188 | "mimetype": "text/x-python", 189 | "name": "python", 190 | "nbconvert_exporter": "python", 191 | "pygments_lexer": "ipython3", 192 | "version": "3.5.0" 193 | } 194 | }, 195 | "nbformat": 4, 196 | "nbformat_minor": 0 197 | } 198 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 21 - Rank and Sort-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "\n", 14 | "from pandas import Series,DataFrame" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 3, 20 | "metadata": { 21 | "collapsed": false 22 | }, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/plain": [ 27 | "C 0\n", 28 | "A 1\n", 29 | "B 2\n", 30 | "dtype: int64" 31 | ] 32 | }, 33 | "execution_count": 3, 34 | "metadata": {}, 35 | "output_type": "execute_result" 36 | } 37 | ], 38 | "source": [ 39 | "ser1 = Series(list(range(3)),index=['C','A','B'])\n", 40 | "ser1" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 4, 46 | "metadata": { 47 | "collapsed": false 48 | }, 49 | "outputs": [ 50 | { 51 | "data": { 52 | "text/plain": [ 53 | "A 1\n", 54 | "B 2\n", 55 | "C 0\n", 56 | "dtype: int64" 57 | ] 58 | }, 59 | "execution_count": 4, 60 | "metadata": {}, 61 | "output_type": "execute_result" 62 | } 63 | ], 64 | "source": [ 65 | "ser1.sort_index()" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 6, 71 | "metadata": { 72 | "collapsed": false 73 | }, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "text/plain": [ 78 | "C 0\n", 79 | "A 1\n", 80 | "B 2\n", 81 | "dtype: int64" 82 | ] 83 | }, 84 | "execution_count": 6, 85 | "metadata": {}, 86 | "output_type": "execute_result" 87 | } 88 | ], 89 | "source": [ 90 | "ser1.sort_values() #he used ser1.order" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 7, 96 | "metadata": { 97 | "collapsed": true 98 | }, 99 | "outputs": [], 100 | "source": [ 101 | "from numpy.random import randn" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 8, 107 | "metadata": { 108 | "collapsed": false 109 | }, 110 | "outputs": [ 111 | { 112 | "data": { 113 | "text/plain": [ 114 | "0 -0.588250\n", 115 | "1 0.467589\n", 116 | "2 0.833443\n", 117 | "3 -0.908241\n", 118 | "4 1.413211\n", 119 | "5 0.389749\n", 120 | "6 0.275788\n", 121 | "7 -0.478224\n", 122 | "8 0.953781\n", 123 | "9 1.873889\n", 124 | "dtype: float64" 125 | ] 126 | }, 127 | "execution_count": 8, 128 | "metadata": {}, 129 | "output_type": "execute_result" 130 | } 131 | ], 132 | "source": [ 133 | "ser2 = Series(randn(10))\n", 134 | "ser2" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 12, 140 | "metadata": { 141 | "collapsed": false 142 | }, 143 | "outputs": [ 144 | { 145 | "data": { 146 | "text/plain": [ 147 | "3 -0.908241\n", 148 | "0 -0.588250\n", 149 | "7 -0.478224\n", 150 | "6 0.275788\n", 151 | "5 0.389749\n", 152 | "1 0.467589\n", 153 | "2 0.833443\n", 154 | "8 0.953781\n", 155 | "4 1.413211\n", 156 | "9 1.873889\n", 157 | "dtype: float64" 158 | ] 159 | }, 160 | "execution_count": 12, 161 | "metadata": {}, 162 | "output_type": "execute_result" 163 | } 164 | ], 165 | "source": [ 166 | "ser2.sort_values(inplace=True)\n", 167 | "ser2" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 13, 173 | "metadata": { 174 | "collapsed": false 175 | }, 176 | "outputs": [ 177 | { 178 | "data": { 179 | "text/plain": [ 180 | "3 1\n", 181 | "0 2\n", 182 | "7 3\n", 183 | "6 4\n", 184 | "5 5\n", 185 | "1 6\n", 186 | "2 7\n", 187 | "8 8\n", 188 | "4 9\n", 189 | "9 10\n", 190 | "dtype: float64" 191 | ] 192 | }, 193 | "execution_count": 13, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "ser2.rank()" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 15, 205 | "metadata": { 206 | "collapsed": false 207 | }, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/plain": [ 212 | "0 0.160484\n", 213 | "1 -0.019495\n", 214 | "2 -1.263092\n", 215 | "3 -0.072723\n", 216 | "4 -1.140240\n", 217 | "5 -1.543357\n", 218 | "6 0.516009\n", 219 | "7 -1.584327\n", 220 | "8 0.112761\n", 221 | "9 1.702406\n", 222 | "dtype: float64" 223 | ] 224 | }, 225 | "execution_count": 15, 226 | "metadata": {}, 227 | "output_type": "execute_result" 228 | } 229 | ], 230 | "source": [ 231 | "ser3 = Series(randn(10))\n", 232 | "ser3" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 16, 238 | "metadata": { 239 | "collapsed": false 240 | }, 241 | "outputs": [ 242 | { 243 | "data": { 244 | "text/plain": [ 245 | "0 8\n", 246 | "1 6\n", 247 | "2 3\n", 248 | "3 5\n", 249 | "4 4\n", 250 | "5 2\n", 251 | "6 9\n", 252 | "7 1\n", 253 | "8 7\n", 254 | "9 10\n", 255 | "dtype: float64" 256 | ] 257 | }, 258 | "execution_count": 16, 259 | "metadata": {}, 260 | "output_type": "execute_result" 261 | } 262 | ], 263 | "source": [ 264 | "ser3.rank()" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 17, 270 | "metadata": { 271 | "collapsed": false 272 | }, 273 | "outputs": [ 274 | { 275 | "data": { 276 | "text/plain": [ 277 | "7 1\n", 278 | "5 2\n", 279 | "2 3\n", 280 | "4 4\n", 281 | "3 5\n", 282 | "1 6\n", 283 | "8 7\n", 284 | "0 8\n", 285 | "6 9\n", 286 | "9 10\n", 287 | "dtype: float64" 288 | ] 289 | }, 290 | "execution_count": 17, 291 | "metadata": {}, 292 | "output_type": "execute_result" 293 | } 294 | ], 295 | "source": [ 296 | "ser3.sort_values(inplace=True)\n", 297 | "ser3.rank()" 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 18, 303 | "metadata": { 304 | "collapsed": false 305 | }, 306 | "outputs": [ 307 | { 308 | "data": { 309 | "text/plain": [ 310 | "7 -1.584327\n", 311 | "5 -1.543357\n", 312 | "2 -1.263092\n", 313 | "4 -1.140240\n", 314 | "3 -0.072723\n", 315 | "1 -0.019495\n", 316 | "8 0.112761\n", 317 | "0 0.160484\n", 318 | "6 0.516009\n", 319 | "9 1.702406\n", 320 | "dtype: float64" 321 | ] 322 | }, 323 | "execution_count": 18, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "ser3" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": { 336 | "collapsed": true 337 | }, 338 | "outputs": [], 339 | "source": [] 340 | } 341 | ], 342 | "metadata": { 343 | "kernelspec": { 344 | "display_name": "Python 3", 345 | "language": "python", 346 | "name": "python3" 347 | }, 348 | "language_info": { 349 | "codemirror_mode": { 350 | "name": "ipython", 351 | "version": 3 352 | }, 353 | "file_extension": ".py", 354 | "mimetype": "text/x-python", 355 | "name": "python", 356 | "nbconvert_exporter": "python", 357 | "pygments_lexer": "ipython3", 358 | "version": "3.5.0" 359 | } 360 | }, 361 | "nbformat": 4, 362 | "nbformat_minor": 0 363 | } 364 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 26 - JSON with Python-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series,DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "# Heres an example of what a JSON (JavaScript Object Notation) looks like:\n", 25 | "json_obj = \"\"\"\n", 26 | "{ \"zoo_animal\": \"Lion\",\n", 27 | " \"food\": [\"Meat\", \"Veggies\", \"Honey\"],\n", 28 | " \"fur\": \"Golden\",\n", 29 | " \"clothes\": null, \n", 30 | " \"diet\": [{\"zoo_animal\": \"Gazelle\", \"food\":\"grass\", \"fur\": \"Brown\"}]\n", 31 | "}\n", 32 | "\"\"\"" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 3, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "import json" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 4, 49 | "metadata": { 50 | "collapsed": true 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "data = json.loads(json_obj)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 5, 60 | "metadata": { 61 | "collapsed": false 62 | }, 63 | "outputs": [ 64 | { 65 | "data": { 66 | "text/plain": [ 67 | "{'clothes': None,\n", 68 | " 'diet': [{'food': 'grass', 'fur': 'Brown', 'zoo_animal': 'Gazelle'}],\n", 69 | " 'food': ['Meat', 'Veggies', 'Honey'],\n", 70 | " 'fur': 'Golden',\n", 71 | " 'zoo_animal': 'Lion'}" 72 | ] 73 | }, 74 | "execution_count": 5, 75 | "metadata": {}, 76 | "output_type": "execute_result" 77 | } 78 | ], 79 | "source": [ 80 | "data" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 6, 86 | "metadata": { 87 | "collapsed": false 88 | }, 89 | "outputs": [ 90 | { 91 | "data": { 92 | "text/plain": [ 93 | "'{\"food\": [\"Meat\", \"Veggies\", \"Honey\"], \"clothes\": null, \"zoo_animal\": \"Lion\", \"fur\": \"Golden\", \"diet\": [{\"food\": \"grass\", \"zoo_animal\": \"Gazelle\", \"fur\": \"Brown\"}]}'" 94 | ] 95 | }, 96 | "execution_count": 6, 97 | "metadata": {}, 98 | "output_type": "execute_result" 99 | } 100 | ], 101 | "source": [ 102 | "json.dumps(data)" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 7, 108 | "metadata": { 109 | "collapsed": false 110 | }, 111 | "outputs": [ 112 | { 113 | "data": { 114 | "text/html": [ 115 | "
\n", 116 | "\n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | "
foodfurzoo_animal
0grassBrownGazelle
\n", 134 | "
" 135 | ], 136 | "text/plain": [ 137 | " food fur zoo_animal\n", 138 | "0 grass Brown Gazelle" 139 | ] 140 | }, 141 | "execution_count": 7, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "dframe = DataFrame(data['diet'])\n", 148 | "dframe" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "metadata": { 155 | "collapsed": true 156 | }, 157 | "outputs": [], 158 | "source": [] 159 | } 160 | ], 161 | "metadata": { 162 | "kernelspec": { 163 | "display_name": "Python 3", 164 | "language": "python", 165 | "name": "python3" 166 | }, 167 | "language_info": { 168 | "codemirror_mode": { 169 | "name": "ipython", 170 | "version": 3 171 | }, 172 | "file_extension": ".py", 173 | "mimetype": "text/x-python", 174 | "name": "python", 175 | "nbconvert_exporter": "python", 176 | "pygments_lexer": "ipython3", 177 | "version": "3.5.0" 178 | } 179 | }, 180 | "nbformat": 4, 181 | "nbformat_minor": 0 182 | } 183 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 28 - Excel with Python-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "# pip install xlrd\n", 23 | "# pip install openpyxl" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 6, 29 | "metadata": { 30 | "collapsed": false 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "xlsfile = pd.ExcelFile('Lec_28_test.xlsx')" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 7, 40 | "metadata": { 41 | "collapsed": true 42 | }, 43 | "outputs": [], 44 | "source": [ 45 | "dframe = xlsfile.parse('Sheet1')" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 9, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/html": [ 58 | "
\n", 59 | "\n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | "
This is a testUnnamed: 1Unnamed: 2
0236678456
1234679456
22347345
33456234
454564365
\n", 101 | "
" 102 | ], 103 | "text/plain": [ 104 | " This is a test Unnamed: 1 Unnamed: 2\n", 105 | "0 23 6678 456\n", 106 | "1 234 679 456\n", 107 | "2 234 7 345\n", 108 | "3 34 56 234\n", 109 | "4 5 456 4365" 110 | ] 111 | }, 112 | "execution_count": 9, 113 | "metadata": {}, 114 | "output_type": "execute_result" 115 | } 116 | ], 117 | "source": [ 118 | "dframe" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": { 125 | "collapsed": true 126 | }, 127 | "outputs": [], 128 | "source": [ 129 | "#Check documentation for more functionality" 130 | ] 131 | } 132 | ], 133 | "metadata": { 134 | "kernelspec": { 135 | "display_name": "Python 3", 136 | "language": "python", 137 | "name": "python3" 138 | }, 139 | "language_info": { 140 | "codemirror_mode": { 141 | "name": "ipython", 142 | "version": 3 143 | }, 144 | "file_extension": ".py", 145 | "mimetype": "text/x-python", 146 | "name": "python", 147 | "nbconvert_exporter": "python", 148 | "pygments_lexer": "ipython3", 149 | "version": "3.5.0" 150 | } 151 | }, 152 | "nbformat": 4, 153 | "nbformat_minor": 0 154 | } 155 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 34 - Pivoting-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "# Lets create some data to play with:\n", 25 | "\n", 26 | "# Note: It is not necessary to understand how this dataset was made to understand this Lecture.\n", 27 | "\n", 28 | "#import pandas testing utility\n", 29 | "import pandas.util.testing as tm; tm.N = 3\n", 30 | "\n", 31 | "#Create a unpivoted function\n", 32 | "def unpivot(frame):\n", 33 | " N, K = frame.shape\n", 34 | " \n", 35 | " data = {'value' : frame.values.ravel('F'),\n", 36 | " 'variable' : np.asarray(frame.columns).repeat(N),\n", 37 | " 'date' : np.tile(np.asarray(frame.index), K)}\n", 38 | " \n", 39 | " # Return the DataFrame\n", 40 | " return DataFrame(data, columns=['date', 'variable', 'value'])\n", 41 | "\n", 42 | "#Set the DataFrame we'll be using\n", 43 | "dframe = unpivot(tm.makeTimeDataFrame())" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 3, 49 | "metadata": { 50 | "collapsed": false 51 | }, 52 | "outputs": [ 53 | { 54 | "data": { 55 | "text/html": [ 56 | "
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datevariablevalue
02000-01-03A-1.666983
12000-01-04A0.843495
22000-01-05A1.485442
32000-01-03B1.026882
42000-01-04B0.443947
52000-01-05B-1.769116
62000-01-03C-0.688001
72000-01-04C-0.700608
82000-01-05C0.235844
92000-01-03D-1.049051
102000-01-04D-0.450190
112000-01-05D-1.827101
\n", 141 | "
" 142 | ], 143 | "text/plain": [ 144 | " date variable value\n", 145 | "0 2000-01-03 A -1.666983\n", 146 | "1 2000-01-04 A 0.843495\n", 147 | "2 2000-01-05 A 1.485442\n", 148 | "3 2000-01-03 B 1.026882\n", 149 | "4 2000-01-04 B 0.443947\n", 150 | "5 2000-01-05 B -1.769116\n", 151 | "6 2000-01-03 C -0.688001\n", 152 | "7 2000-01-04 C -0.700608\n", 153 | "8 2000-01-05 C 0.235844\n", 154 | "9 2000-01-03 D -1.049051\n", 155 | "10 2000-01-04 D -0.450190\n", 156 | "11 2000-01-05 D -1.827101" 157 | ] 158 | }, 159 | "execution_count": 3, 160 | "metadata": {}, 161 | "output_type": "execute_result" 162 | } 163 | ], 164 | "source": [ 165 | "dframe" 166 | ] 167 | }, 168 | { 169 | "cell_type": "code", 170 | "execution_count": 4, 171 | "metadata": { 172 | "collapsed": true 173 | }, 174 | "outputs": [], 175 | "source": [ 176 | "dframe_piv = dframe.pivot('date','variable','value')" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 5, 182 | "metadata": { 183 | "collapsed": false 184 | }, 185 | "outputs": [ 186 | { 187 | "data": { 188 | "text/html": [ 189 | "
\n", 190 | "\n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | "
variableABCD
date
2000-01-03-1.6669831.026882-0.688001-1.049051
2000-01-040.8434950.443947-0.700608-0.450190
2000-01-051.485442-1.7691160.235844-1.827101
\n", 231 | "
" 232 | ], 233 | "text/plain": [ 234 | "variable A B C D\n", 235 | "date \n", 236 | "2000-01-03 -1.666983 1.026882 -0.688001 -1.049051\n", 237 | "2000-01-04 0.843495 0.443947 -0.700608 -0.450190\n", 238 | "2000-01-05 1.485442 -1.769116 0.235844 -1.827101" 239 | ] 240 | }, 241 | "execution_count": 5, 242 | "metadata": {}, 243 | "output_type": "execute_result" 244 | } 245 | ], 246 | "source": [ 247 | "dframe_piv" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": null, 253 | "metadata": { 254 | "collapsed": true 255 | }, 256 | "outputs": [], 257 | "source": [] 258 | } 259 | ], 260 | "metadata": { 261 | "kernelspec": { 262 | "display_name": "Python 3", 263 | "language": "python", 264 | "name": "python3" 265 | }, 266 | "language_info": { 267 | "codemirror_mode": { 268 | "name": "ipython", 269 | "version": 3 270 | }, 271 | "file_extension": ".py", 272 | "mimetype": "text/x-python", 273 | "name": "python", 274 | "nbconvert_exporter": "python", 275 | "pygments_lexer": "ipython3", 276 | "version": "3.5.0" 277 | } 278 | }, 279 | "nbformat": 4, 280 | "nbformat_minor": 0 281 | } 282 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 35 - Duplicates in DataFrames-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series,DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/html": [ 26 | "
\n", 27 | "\n", 28 | " \n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | "
key1key2
0A2
1A2
2B2
3B3
4B3
\n", 63 | "
" 64 | ], 65 | "text/plain": [ 66 | " key1 key2\n", 67 | "0 A 2\n", 68 | "1 A 2\n", 69 | "2 B 2\n", 70 | "3 B 3\n", 71 | "4 B 3" 72 | ] 73 | }, 74 | "execution_count": 3, 75 | "metadata": {}, 76 | "output_type": "execute_result" 77 | } 78 | ], 79 | "source": [ 80 | "dframe = DataFrame({'key1': ['A'] * 2 + ['B'] * 3,\n", 81 | " 'key2': [2, 2, 2, 3, 3]})\n", 82 | "dframe" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 5, 88 | "metadata": { 89 | "collapsed": false 90 | }, 91 | "outputs": [ 92 | { 93 | "data": { 94 | "text/plain": [ 95 | "0 False\n", 96 | "1 True\n", 97 | "2 False\n", 98 | "3 False\n", 99 | "4 True\n", 100 | "dtype: bool" 101 | ] 102 | }, 103 | "execution_count": 5, 104 | "metadata": {}, 105 | "output_type": "execute_result" 106 | } 107 | ], 108 | "source": [ 109 | "dframe.duplicated()" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 6, 115 | "metadata": { 116 | "collapsed": false 117 | }, 118 | "outputs": [ 119 | { 120 | "data": { 121 | "text/html": [ 122 | "
\n", 123 | "\n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | "
key1key2
0A2
2B2
3B3
\n", 149 | "
" 150 | ], 151 | "text/plain": [ 152 | " key1 key2\n", 153 | "0 A 2\n", 154 | "2 B 2\n", 155 | "3 B 3" 156 | ] 157 | }, 158 | "execution_count": 6, 159 | "metadata": {}, 160 | "output_type": "execute_result" 161 | } 162 | ], 163 | "source": [ 164 | "dframe.drop_duplicates()" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": 7, 170 | "metadata": { 171 | "collapsed": false 172 | }, 173 | "outputs": [ 174 | { 175 | "data": { 176 | "text/html": [ 177 | "
\n", 178 | "\n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | "
key1key2
0A2
2B2
\n", 199 | "
" 200 | ], 201 | "text/plain": [ 202 | " key1 key2\n", 203 | "0 A 2\n", 204 | "2 B 2" 205 | ] 206 | }, 207 | "execution_count": 7, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "dframe.drop_duplicates(['key1'])" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 8, 219 | "metadata": { 220 | "collapsed": false 221 | }, 222 | "outputs": [ 223 | { 224 | "data": { 225 | "text/html": [ 226 | "
\n", 227 | "\n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | "
key1key2
0A2
1A2
2B2
3B3
4B3
\n", 263 | "
" 264 | ], 265 | "text/plain": [ 266 | " key1 key2\n", 267 | "0 A 2\n", 268 | "1 A 2\n", 269 | "2 B 2\n", 270 | "3 B 3\n", 271 | "4 B 3" 272 | ] 273 | }, 274 | "execution_count": 8, 275 | "metadata": {}, 276 | "output_type": "execute_result" 277 | } 278 | ], 279 | "source": [ 280 | "dframe" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": 12, 286 | "metadata": { 287 | "collapsed": false 288 | }, 289 | "outputs": [ 290 | { 291 | "data": { 292 | "text/html": [ 293 | "
\n", 294 | "\n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | "
key1key2
1A2
4B3
\n", 315 | "
" 316 | ], 317 | "text/plain": [ 318 | " key1 key2\n", 319 | "1 A 2\n", 320 | "4 B 3" 321 | ] 322 | }, 323 | "execution_count": 12, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "dframe.drop_duplicates(['key1'], keep=\"last\")" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": { 336 | "collapsed": true 337 | }, 338 | "outputs": [], 339 | "source": [] 340 | } 341 | ], 342 | "metadata": { 343 | "kernelspec": { 344 | "display_name": "Python 3", 345 | "language": "python", 346 | "name": "python3" 347 | }, 348 | "language_info": { 349 | "codemirror_mode": { 350 | "name": "ipython", 351 | "version": 3 352 | }, 353 | "file_extension": ".py", 354 | "mimetype": "text/x-python", 355 | "name": "python", 356 | "nbconvert_exporter": "python", 357 | "pygments_lexer": "ipython3", 358 | "version": "3.5.0" 359 | } 360 | }, 361 | "nbformat": 4, 362 | "nbformat_minor": 0 363 | } 364 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 36 - Mapping-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/html": [ 26 | "
\n", 27 | "\n", 28 | " \n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | "
altitudecity
03158Alma
13000Brian Head
22762Fox Park
\n", 53 | "
" 54 | ], 55 | "text/plain": [ 56 | " altitude city\n", 57 | "0 3158 Alma\n", 58 | "1 3000 Brian Head\n", 59 | "2 2762 Fox Park" 60 | ] 61 | }, 62 | "execution_count": 2, 63 | "metadata": {}, 64 | "output_type": "execute_result" 65 | } 66 | ], 67 | "source": [ 68 | "dframe = DataFrame({'city':['Alma','Brian Head','Fox Park'],\n", 69 | " 'altitude':[3158,3000,2762]})\n", 70 | "dframe" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 3, 76 | "metadata": { 77 | "collapsed": true 78 | }, 79 | "outputs": [], 80 | "source": [ 81 | "state_map = {'Alma':'Colorado','Brian Head':'Utah','Fox Park':'Wyoming'}" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 4, 87 | "metadata": { 88 | "collapsed": false 89 | }, 90 | "outputs": [ 91 | { 92 | "data": { 93 | "text/html": [ 94 | "
\n", 95 | "\n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | "
altitudecitystate
03158AlmaColorado
13000Brian HeadUtah
22762Fox ParkWyoming
\n", 125 | "
" 126 | ], 127 | "text/plain": [ 128 | " altitude city state\n", 129 | "0 3158 Alma Colorado\n", 130 | "1 3000 Brian Head Utah\n", 131 | "2 2762 Fox Park Wyoming" 132 | ] 133 | }, 134 | "execution_count": 4, 135 | "metadata": {}, 136 | "output_type": "execute_result" 137 | } 138 | ], 139 | "source": [ 140 | "dframe['state'] = dframe['city'].map(state_map)\n", 141 | "dframe" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": null, 147 | "metadata": { 148 | "collapsed": true 149 | }, 150 | "outputs": [], 151 | "source": [] 152 | } 153 | ], 154 | "metadata": { 155 | "kernelspec": { 156 | "display_name": "Python 3", 157 | "language": "python", 158 | "name": "python3" 159 | }, 160 | "language_info": { 161 | "codemirror_mode": { 162 | "name": "ipython", 163 | "version": 3 164 | }, 165 | "file_extension": ".py", 166 | "mimetype": "text/x-python", 167 | "name": "python", 168 | "nbconvert_exporter": "python", 169 | "pygments_lexer": "ipython3", 170 | "version": "3.5.0" 171 | } 172 | }, 173 | "nbformat": 4, 174 | "nbformat_minor": 0 175 | } 176 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 37 - Replace-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/plain": [ 26 | "0 1\n", 27 | "1 2\n", 28 | "2 3\n", 29 | "3 4\n", 30 | "4 1\n", 31 | "5 2\n", 32 | "6 3\n", 33 | "7 4\n", 34 | "dtype: int64" 35 | ] 36 | }, 37 | "execution_count": 2, 38 | "metadata": {}, 39 | "output_type": "execute_result" 40 | } 41 | ], 42 | "source": [ 43 | "ser1 = Series([1,2,3,4,1,2,3,4])\n", 44 | "ser1" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "0 NaN\n", 58 | "1 2\n", 59 | "2 3\n", 60 | "3 4\n", 61 | "4 NaN\n", 62 | "5 2\n", 63 | "6 3\n", 64 | "7 4\n", 65 | "dtype: float64" 66 | ] 67 | }, 68 | "execution_count": 3, 69 | "metadata": {}, 70 | "output_type": "execute_result" 71 | } 72 | ], 73 | "source": [ 74 | "ser1.replace(1,np.nan)" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 4, 80 | "metadata": { 81 | "collapsed": false 82 | }, 83 | "outputs": [ 84 | { 85 | "data": { 86 | "text/plain": [ 87 | "0 100\n", 88 | "1 2\n", 89 | "2 3\n", 90 | "3 400\n", 91 | "4 100\n", 92 | "5 2\n", 93 | "6 3\n", 94 | "7 400\n", 95 | "dtype: int64" 96 | ] 97 | }, 98 | "execution_count": 4, 99 | "metadata": {}, 100 | "output_type": "execute_result" 101 | } 102 | ], 103 | "source": [ 104 | "ser1.replace([1,4],[100,400])" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 5, 110 | "metadata": { 111 | "collapsed": false 112 | }, 113 | "outputs": [ 114 | { 115 | "data": { 116 | "text/plain": [ 117 | "0 1\n", 118 | "1 2\n", 119 | "2 3\n", 120 | "3 NaN\n", 121 | "4 1\n", 122 | "5 2\n", 123 | "6 3\n", 124 | "7 NaN\n", 125 | "dtype: float64" 126 | ] 127 | }, 128 | "execution_count": 5, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "ser1.replace({4:np.nan})" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": { 141 | "collapsed": true 142 | }, 143 | "outputs": [], 144 | "source": [] 145 | } 146 | ], 147 | "metadata": { 148 | "kernelspec": { 149 | "display_name": "Python 3", 150 | "language": "python", 151 | "name": "python3" 152 | }, 153 | "language_info": { 154 | "codemirror_mode": { 155 | "name": "ipython", 156 | "version": 3 157 | }, 158 | "file_extension": ".py", 159 | "mimetype": "text/x-python", 160 | "name": "python", 161 | "nbconvert_exporter": "python", 162 | "pygments_lexer": "ipython3", 163 | "version": "3.5.0" 164 | } 165 | }, 166 | "nbformat": 4, 167 | "nbformat_minor": 0 168 | } 169 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 39 - Binning-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "years = [1990,1991,1992,2008,2012,2015,1987,1969,2013,2008,1999]" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 3, 30 | "metadata": { 31 | "collapsed": true 32 | }, 33 | "outputs": [], 34 | "source": [ 35 | "decade_bins = [1960,1970,1980,1990,2000,2010,2020]" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 4, 41 | "metadata": { 42 | "collapsed": true 43 | }, 44 | "outputs": [], 45 | "source": [ 46 | "decade_cat = pd.cut(years,decade_bins)" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 5, 52 | "metadata": { 53 | "collapsed": false 54 | }, 55 | "outputs": [ 56 | { 57 | "data": { 58 | "text/plain": [ 59 | "[(1980, 1990], (1990, 2000], (1990, 2000], (2000, 2010], (2010, 2020], ..., (1980, 1990], (1960, 1970], (2010, 2020], (2000, 2010], (1990, 2000]]\n", 60 | "Length: 11\n", 61 | "Categories (6, object): [(1960, 1970] < (1970, 1980] < (1980, 1990] < (1990, 2000] < (2000, 2010] < (2010, 2020]]" 62 | ] 63 | }, 64 | "execution_count": 5, 65 | "metadata": {}, 66 | "output_type": "execute_result" 67 | } 68 | ], 69 | "source": [ 70 | "decade_cat" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 6, 76 | "metadata": { 77 | "collapsed": false 78 | }, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/plain": [ 83 | "Index(['(1960, 1970]', '(1970, 1980]', '(1980, 1990]', '(1990, 2000]',\n", 84 | " '(2000, 2010]', '(2010, 2020]'],\n", 85 | " dtype='object')" 86 | ] 87 | }, 88 | "execution_count": 6, 89 | "metadata": {}, 90 | "output_type": "execute_result" 91 | } 92 | ], 93 | "source": [ 94 | "decade_cat.categories" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 7, 100 | "metadata": { 101 | "collapsed": false 102 | }, 103 | "outputs": [ 104 | { 105 | "data": { 106 | "text/plain": [ 107 | "(2010, 2020] 3\n", 108 | "(1990, 2000] 3\n", 109 | "(2000, 2010] 2\n", 110 | "(1980, 1990] 2\n", 111 | "(1960, 1970] 1\n", 112 | "(1970, 1980] 0\n", 113 | "dtype: int64" 114 | ] 115 | }, 116 | "execution_count": 7, 117 | "metadata": {}, 118 | "output_type": "execute_result" 119 | } 120 | ], 121 | "source": [ 122 | "pd.value_counts(decade_cat)" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 8, 128 | "metadata": { 129 | "collapsed": false 130 | }, 131 | "outputs": [ 132 | { 133 | "data": { 134 | "text/plain": [ 135 | "[(1969, 1992], (1969, 1992], (1969, 1992], (1992, 2015], (1992, 2015], ..., (1969, 1992], (1969, 1992], (1992, 2015], (1992, 2015], (1992, 2015]]\n", 136 | "Length: 11\n", 137 | "Categories (2, object): [(1969, 1992] < (1992, 2015]]" 138 | ] 139 | }, 140 | "execution_count": 8, 141 | "metadata": {}, 142 | "output_type": "execute_result" 143 | } 144 | ], 145 | "source": [ 146 | "pd.cut(years,2,precision=1)" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": null, 152 | "metadata": { 153 | "collapsed": true 154 | }, 155 | "outputs": [], 156 | "source": [] 157 | } 158 | ], 159 | "metadata": { 160 | "kernelspec": { 161 | "display_name": "Python 3", 162 | "language": "python", 163 | "name": "python3" 164 | }, 165 | "language_info": { 166 | "codemirror_mode": { 167 | "name": "ipython", 168 | "version": 3 169 | }, 170 | "file_extension": ".py", 171 | "mimetype": "text/x-python", 172 | "name": "python", 173 | "nbconvert_exporter": "python", 174 | "pygments_lexer": "ipython3", 175 | "version": "3.5.0" 176 | } 177 | }, 178 | "nbformat": 4, 179 | "nbformat_minor": 0 180 | } 181 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 41 - Permutation-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "dframe = DataFrame(np.arange(16).reshape((4, 4)))" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 4, 30 | "metadata": { 31 | "collapsed": true 32 | }, 33 | "outputs": [], 34 | "source": [ 35 | "blender = np.random.permutation(4)" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 5, 41 | "metadata": { 42 | "collapsed": false 43 | }, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/plain": [ 48 | "array([3, 2, 0, 1])" 49 | ] 50 | }, 51 | "execution_count": 5, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | } 55 | ], 56 | "source": [ 57 | "blender" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 6, 63 | "metadata": { 64 | "collapsed": false 65 | }, 66 | "outputs": [ 67 | { 68 | "data": { 69 | "text/html": [ 70 | "
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" 184 | ], 185 | "text/plain": [ 186 | " 0 1 2 3\n", 187 | "3 12 13 14 15\n", 188 | "2 8 9 10 11\n", 189 | "0 0 1 2 3\n", 190 | "1 4 5 6 7" 191 | ] 192 | }, 193 | "execution_count": 7, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "dframe.take(blender)" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 9, 205 | "metadata": { 206 | "collapsed": false 207 | }, 208 | "outputs": [], 209 | "source": [ 210 | "box = np.array([1,2,3])" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 10, 216 | "metadata": { 217 | "collapsed": true 218 | }, 219 | "outputs": [], 220 | "source": [ 221 | "shaker = np.random.randint(0,len(box),size=10)" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 11, 227 | "metadata": { 228 | "collapsed": false 229 | }, 230 | "outputs": [ 231 | { 232 | "data": { 233 | "text/plain": [ 234 | "array([2, 0, 2, 2, 1, 1, 0, 2, 0, 0])" 235 | ] 236 | }, 237 | "execution_count": 11, 238 | "metadata": {}, 239 | "output_type": "execute_result" 240 | } 241 | ], 242 | "source": [ 243 | "shaker" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 14, 249 | "metadata": { 250 | "collapsed": false 251 | }, 252 | "outputs": [ 253 | { 254 | "data": { 255 | "text/plain": [ 256 | "array([3, 1, 3, 3, 2, 2, 1, 3, 1, 1])" 257 | ] 258 | }, 259 | "execution_count": 14, 260 | "metadata": {}, 261 | "output_type": "execute_result" 262 | } 263 | ], 264 | "source": [ 265 | "hand_grabs = box.take(shaker)\n", 266 | "hand_grabs" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": null, 272 | "metadata": { 273 | "collapsed": true 274 | }, 275 | "outputs": [], 276 | "source": [] 277 | } 278 | ], 279 | "metadata": { 280 | "kernelspec": { 281 | "display_name": "Python 3", 282 | "language": "python", 283 | "name": "python3" 284 | }, 285 | "language_info": { 286 | "codemirror_mode": { 287 | "name": "ipython", 288 | "version": 3 289 | }, 290 | "file_extension": ".py", 291 | "mimetype": "text/x-python", 292 | "name": "python", 293 | "nbconvert_exporter": "python", 294 | "pygments_lexer": "ipython3", 295 | "version": "3.5.0" 296 | } 297 | }, 298 | "nbformat": 4, 299 | "nbformat_minor": 0 300 | } 301 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 46 - Cross Tabulation-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 4, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "from io import StringIO #Python 2 from StringIO import StringIO" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 5, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "data=\"\"\"\\\n", 34 | "Sample Animal Intelligence\n", 35 | "1 Dog Smart\n", 36 | "2 Dog Smart\n", 37 | "3 Cat Dumb\n", 38 | "4 Cat Dumb\n", 39 | "5 Dog Dumb\n", 40 | "6 Cat Smart\"\"\"" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 6, 46 | "metadata": { 47 | "collapsed": true 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "dframe = pd.read_table(StringIO(data),sep='\\s+')" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 7, 57 | "metadata": { 58 | "collapsed": false 59 | }, 60 | "outputs": [ 61 | { 62 | "data": { 63 | "text/html": [ 64 | "
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" 182 | ], 183 | "text/plain": [ 184 | "Intelligence Dumb Smart All\n", 185 | "Animal \n", 186 | "Cat 2 1 3\n", 187 | "Dog 1 2 3\n", 188 | "All 3 3 6" 189 | ] 190 | }, 191 | "execution_count": 9, 192 | "metadata": {}, 193 | "output_type": "execute_result" 194 | } 195 | ], 196 | "source": [ 197 | "pd.crosstab(dframe.Animal,dframe.Intelligence, margins=True)" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": null, 203 | "metadata": { 204 | "collapsed": true 205 | }, 206 | "outputs": [], 207 | "source": [] 208 | } 209 | ], 210 | "metadata": { 211 | "kernelspec": { 212 | "display_name": "Python 3", 213 | "language": "python", 214 | "name": "python3" 215 | }, 216 | "language_info": { 217 | "codemirror_mode": { 218 | "name": "ipython", 219 | "version": 3 220 | }, 221 | "file_extension": ".py", 222 | "mimetype": "text/x-python", 223 | "name": "python", 224 | "nbconvert_exporter": "python", 225 | "pygments_lexer": "ipython3", 226 | "version": "3.5.0" 227 | } 228 | }, 229 | "nbformat": 4, 230 | "nbformat_minor": 0 231 | } 232 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 47 - Installing Seaborn-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "#http://stanford.edu/~mwaskom/software/seaborn/installing.html" 12 | ] 13 | } 14 | ], 15 | "metadata": { 16 | "kernelspec": { 17 | "display_name": "Python 3", 18 | "language": "python", 19 | "name": "python3" 20 | }, 21 | "language_info": { 22 | "codemirror_mode": { 23 | "name": "ipython", 24 | "version": 3 25 | }, 26 | "file_extension": ".py", 27 | "mimetype": "text/x-python", 28 | "name": "python", 29 | "nbconvert_exporter": "python", 30 | "pygments_lexer": "ipython3", 31 | "version": "3.5.0" 32 | } 33 | }, 34 | "nbformat": 4, 35 | "nbformat_minor": 0 36 | } 37 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 7 - Creating arrays-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "my_list1 = [1,2,3,4]" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "my_array1 = np.array(my_list1)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 4, 39 | "metadata": { 40 | "collapsed": false 41 | }, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "text/plain": [ 46 | "array([1, 2, 3, 4])" 47 | ] 48 | }, 49 | "execution_count": 4, 50 | "metadata": {}, 51 | "output_type": "execute_result" 52 | } 53 | ], 54 | "source": [ 55 | "my_array1" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 5, 61 | "metadata": { 62 | "collapsed": true 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "my_list2 = [11,22,33,44]" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 7, 72 | "metadata": { 73 | "collapsed": false 74 | }, 75 | "outputs": [], 76 | "source": [ 77 | "my_lists = [my_list1, my_list2]" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 8, 83 | "metadata": { 84 | "collapsed": true 85 | }, 86 | "outputs": [], 87 | "source": [ 88 | "my_array2 = np.array(my_lists)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 9, 94 | "metadata": { 95 | "collapsed": false 96 | }, 97 | "outputs": [ 98 | { 99 | "data": { 100 | "text/plain": [ 101 | "array([[ 1, 2, 3, 4],\n", 102 | " [11, 22, 33, 44]])" 103 | ] 104 | }, 105 | "execution_count": 9, 106 | "metadata": {}, 107 | "output_type": "execute_result" 108 | } 109 | ], 110 | "source": [ 111 | "my_array2 #what happens if they are not the same length?" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 10, 117 | "metadata": { 118 | "collapsed": false 119 | }, 120 | "outputs": [ 121 | { 122 | "data": { 123 | "text/plain": [ 124 | "(2, 4)" 125 | ] 126 | }, 127 | "execution_count": 10, 128 | "metadata": {}, 129 | "output_type": "execute_result" 130 | } 131 | ], 132 | "source": [ 133 | "my_array2.shape" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 11, 139 | "metadata": { 140 | "collapsed": false 141 | }, 142 | "outputs": [ 143 | { 144 | "data": { 145 | "text/plain": [ 146 | "dtype('int32')" 147 | ] 148 | }, 149 | "execution_count": 11, 150 | "metadata": {}, 151 | "output_type": "execute_result" 152 | } 153 | ], 154 | "source": [ 155 | "my_array2.dtype" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 13, 161 | "metadata": { 162 | "collapsed": false 163 | }, 164 | "outputs": [ 165 | { 166 | "data": { 167 | "text/plain": [ 168 | "array([ 0., 0., 0., 0., 0.])" 169 | ] 170 | }, 171 | "execution_count": 13, 172 | "metadata": {}, 173 | "output_type": "execute_result" 174 | } 175 | ], 176 | "source": [ 177 | "np.zeros(5)" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": 14, 183 | "metadata": { 184 | "collapsed": true 185 | }, 186 | "outputs": [], 187 | "source": [ 188 | "my_zeros_array = np.zeros(5)" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 15, 194 | "metadata": { 195 | "collapsed": false 196 | }, 197 | "outputs": [ 198 | { 199 | "data": { 200 | "text/plain": [ 201 | "dtype('float64')" 202 | ] 203 | }, 204 | "execution_count": 15, 205 | "metadata": {}, 206 | "output_type": "execute_result" 207 | } 208 | ], 209 | "source": [ 210 | "my_zeros_array.dtype" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 16, 216 | "metadata": { 217 | "collapsed": false 218 | }, 219 | "outputs": [ 220 | { 221 | "data": { 222 | "text/plain": [ 223 | "array([[ 1., 1., 1., 1., 1.],\n", 224 | " [ 1., 1., 1., 1., 1.],\n", 225 | " [ 1., 1., 1., 1., 1.],\n", 226 | " [ 1., 1., 1., 1., 1.],\n", 227 | " [ 1., 1., 1., 1., 1.]])" 228 | ] 229 | }, 230 | "execution_count": 16, 231 | "metadata": {}, 232 | "output_type": "execute_result" 233 | } 234 | ], 235 | "source": [ 236 | "np.ones([5,5])" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": 17, 242 | "metadata": { 243 | "collapsed": false 244 | }, 245 | "outputs": [ 246 | { 247 | "data": { 248 | "text/plain": [ 249 | "array([ 0., 0., 0., 0., 0.])" 250 | ] 251 | }, 252 | "execution_count": 17, 253 | "metadata": {}, 254 | "output_type": "execute_result" 255 | } 256 | ], 257 | "source": [ 258 | "np.empty(5)" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": 18, 264 | "metadata": { 265 | "collapsed": false 266 | }, 267 | "outputs": [ 268 | { 269 | "data": { 270 | "text/plain": [ 271 | "array([[ 1., 0., 0., 0., 0.],\n", 272 | " [ 0., 1., 0., 0., 0.],\n", 273 | " [ 0., 0., 1., 0., 0.],\n", 274 | " [ 0., 0., 0., 1., 0.],\n", 275 | " [ 0., 0., 0., 0., 1.]])" 276 | ] 277 | }, 278 | "execution_count": 18, 279 | "metadata": {}, 280 | "output_type": "execute_result" 281 | } 282 | ], 283 | "source": [ 284 | "np.eye(5)" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": 19, 290 | "metadata": { 291 | "collapsed": false 292 | }, 293 | "outputs": [ 294 | { 295 | "data": { 296 | "text/plain": [ 297 | "array([0, 1, 2, 3, 4])" 298 | ] 299 | }, 300 | "execution_count": 19, 301 | "metadata": {}, 302 | "output_type": "execute_result" 303 | } 304 | ], 305 | "source": [ 306 | "np.arange(5)" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 20, 312 | "metadata": { 313 | "collapsed": false 314 | }, 315 | "outputs": [ 316 | { 317 | "data": { 318 | "text/plain": [ 319 | "array([ 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37,\n", 320 | " 39, 41, 43, 45, 47, 49])" 321 | ] 322 | }, 323 | "execution_count": 20, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "np.arange(5,50,2)" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": { 336 | "collapsed": true 337 | }, 338 | "outputs": [], 339 | "source": [] 340 | } 341 | ], 342 | "metadata": { 343 | "kernelspec": { 344 | "display_name": "Python 3", 345 | "language": "python", 346 | "name": "python3" 347 | }, 348 | "language_info": { 349 | "codemirror_mode": { 350 | "name": "ipython", 351 | "version": 3 352 | }, 353 | "file_extension": ".py", 354 | "mimetype": "text/x-python", 355 | "name": "python", 356 | "nbconvert_exporter": "python", 357 | "pygments_lexer": "ipython3", 358 | "version": "3.5.0" 359 | } 360 | }, 361 | "nbformat": 4, 362 | "nbformat_minor": 0 363 | } 364 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Lecture 8 - Using arrays and scalars-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "2.5" 25 | ] 26 | }, 27 | "execution_count": 2, 28 | "metadata": {}, 29 | "output_type": "execute_result" 30 | } 31 | ], 32 | "source": [ 33 | "5/2" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 3, 39 | "metadata": { 40 | "collapsed": true 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "#For Python 2\n", 45 | "#from __future__ import division" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/plain": [ 58 | "2.5" 59 | ] 60 | }, 61 | "execution_count": 4, 62 | "metadata": {}, 63 | "output_type": "execute_result" 64 | } 65 | ], 66 | "source": [ 67 | "5/2" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 5, 73 | "metadata": { 74 | "collapsed": true 75 | }, 76 | "outputs": [], 77 | "source": [ 78 | "arr1 = np.array([[1,2,3,4],[8,9,10,11]])" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 6, 84 | "metadata": { 85 | "collapsed": false 86 | }, 87 | "outputs": [ 88 | { 89 | "data": { 90 | "text/plain": [ 91 | "array([[ 1, 2, 3, 4],\n", 92 | " [ 8, 9, 10, 11]])" 93 | ] 94 | }, 95 | "execution_count": 6, 96 | "metadata": {}, 97 | "output_type": "execute_result" 98 | } 99 | ], 100 | "source": [ 101 | "arr1" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 7, 107 | "metadata": { 108 | "collapsed": false 109 | }, 110 | "outputs": [ 111 | { 112 | "data": { 113 | "text/plain": [ 114 | "array([[ 1, 4, 9, 16],\n", 115 | " [ 64, 81, 100, 121]])" 116 | ] 117 | }, 118 | "execution_count": 7, 119 | "metadata": {}, 120 | "output_type": "execute_result" 121 | } 122 | ], 123 | "source": [ 124 | "arr1*arr1" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 8, 130 | "metadata": { 131 | "collapsed": false 132 | }, 133 | "outputs": [ 134 | { 135 | "data": { 136 | "text/plain": [ 137 | "array([[0, 0, 0, 0],\n", 138 | " [0, 0, 0, 0]])" 139 | ] 140 | }, 141 | "execution_count": 8, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "arr1 - arr1" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 9, 153 | "metadata": { 154 | "collapsed": false 155 | }, 156 | "outputs": [ 157 | { 158 | "data": { 159 | "text/plain": [ 160 | "array([[ 1. , 0.5 , 0.33333333, 0.25 ],\n", 161 | " [ 0.125 , 0.11111111, 0.1 , 0.09090909]])" 162 | ] 163 | }, 164 | "execution_count": 9, 165 | "metadata": {}, 166 | "output_type": "execute_result" 167 | } 168 | ], 169 | "source": [ 170 | "1 / arr1" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 13, 176 | "metadata": { 177 | "collapsed": false 178 | }, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "text/plain": [ 183 | "array([[ 1, 8, 27, 64],\n", 184 | " [ 512, 729, 1000, 1331]], dtype=int32)" 185 | ] 186 | }, 187 | "execution_count": 13, 188 | "metadata": {}, 189 | "output_type": "execute_result" 190 | } 191 | ], 192 | "source": [ 193 | "arr1**3" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "metadata": { 200 | "collapsed": true 201 | }, 202 | "outputs": [], 203 | "source": [] 204 | } 205 | ], 206 | "metadata": { 207 | "kernelspec": { 208 | "display_name": "Python 3", 209 | "language": "python", 210 | "name": "python3" 211 | }, 212 | "language_info": { 213 | "codemirror_mode": { 214 | "name": "ipython", 215 | "version": 3 216 | }, 217 | "file_extension": ".py", 218 | "mimetype": "text/x-python", 219 | "name": "python", 220 | "nbconvert_exporter": "python", 221 | "pygments_lexer": "ipython3", 222 | "version": "3.5.0" 223 | } 224 | }, 225 | "nbformat": 4, 226 | "nbformat_minor": 0 227 | } 228 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Python Overview Part 2-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 10, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "cities = ['NY','LA','SF']" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 11, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "name": "stdout", 23 | "output_type": "stream", 24 | "text": [ 25 | "NY\n", 26 | "LA\n", 27 | "SF\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "for city in cities:\n", 33 | " print(city)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 12, 39 | "metadata": { 40 | "collapsed": false 41 | }, 42 | "outputs": [ 43 | { 44 | "name": "stdout", 45 | "output_type": "stream", 46 | "text": [ 47 | "I love NY\n", 48 | "I love LA\n", 49 | "I love SF\n" 50 | ] 51 | } 52 | ], 53 | "source": [ 54 | "for city in cities:\n", 55 | " phrase = 'I love ' + city\n", 56 | " print(phrase)" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 13, 62 | "metadata": { 63 | "collapsed": false 64 | }, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "The inverse of 1 is 1.0\n", 71 | "The inverse of 2 is 0.5\n", 72 | "The inverse of 3 is 0.3333333333333333\n", 73 | "The inverse of 4 is 0.25\n", 74 | "The inverse of 5 is 0.2\n", 75 | "The inverse of 6 is 0.16666666666666666\n", 76 | "The inverse of 7 is 0.14285714285714285\n", 77 | "The inverse of 8 is 0.125\n", 78 | "The inverse of 9 is 0.1111111111111111\n" 79 | ] 80 | } 81 | ], 82 | "source": [ 83 | "for n in range(1,10):\n", 84 | " print('The inverse of',n, 'is',1/n) # in python 2 use 1.0/n" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 14, 90 | "metadata": { 91 | "collapsed": false 92 | }, 93 | "outputs": [ 94 | { 95 | "name": "stdout", 96 | "output_type": "stream", 97 | "text": [ 98 | "H\n", 99 | "e\n", 100 | "l\n", 101 | "l\n", 102 | "o\n" 103 | ] 104 | } 105 | ], 106 | "source": [ 107 | "for letter in 'Hello':\n", 108 | " print(letter)" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 17, 114 | "metadata": { 115 | "collapsed": false 116 | }, 117 | "outputs": [ 118 | { 119 | "data": { 120 | "text/plain": [ 121 | "'NY'" 122 | ] 123 | }, 124 | "execution_count": 17, 125 | "metadata": {}, 126 | "output_type": "execute_result" 127 | } 128 | ], 129 | "source": [ 130 | "cities[0]" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 28, 136 | "metadata": { 137 | "collapsed": false 138 | }, 139 | "outputs": [ 140 | { 141 | "name": "stdout", 142 | "output_type": "stream", 143 | "text": [ 144 | "party!\n" 145 | ] 146 | } 147 | ], 148 | "source": [ 149 | "if city == 'NY':\n", 150 | " print('party!')\n", 151 | "else:\n", 152 | " print('Work')" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 25, 158 | "metadata": { 159 | "collapsed": false 160 | }, 161 | "outputs": [], 162 | "source": [ 163 | "city='NY' #Note the order doesn't matter ^-----" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 29, 169 | "metadata": { 170 | "collapsed": false 171 | }, 172 | "outputs": [ 173 | { 174 | "data": { 175 | "text/plain": [ 176 | "False" 177 | ] 178 | }, 179 | "execution_count": 29, 180 | "metadata": {}, 181 | "output_type": "execute_result" 182 | } 183 | ], 184 | "source": [ 185 | "1==2" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 30, 191 | "metadata": { 192 | "collapsed": false 193 | }, 194 | "outputs": [ 195 | { 196 | "data": { 197 | "text/plain": [ 198 | "True" 199 | ] 200 | }, 201 | "execution_count": 30, 202 | "metadata": {}, 203 | "output_type": "execute_result" 204 | } 205 | ], 206 | "source": [ 207 | "2 == 2" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 31, 213 | "metadata": { 214 | "collapsed": false 215 | }, 216 | "outputs": [ 217 | { 218 | "data": { 219 | "text/plain": [ 220 | "False" 221 | ] 222 | }, 223 | "execution_count": 31, 224 | "metadata": {}, 225 | "output_type": "execute_result" 226 | } 227 | ], 228 | "source": [ 229 | "3 > 4" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 32, 235 | "metadata": { 236 | "collapsed": false 237 | }, 238 | "outputs": [ 239 | { 240 | "data": { 241 | "text/plain": [ 242 | "True" 243 | ] 244 | }, 245 | "execution_count": 32, 246 | "metadata": {}, 247 | "output_type": "execute_result" 248 | } 249 | ], 250 | "source": [ 251 | "4 < 5" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 33, 257 | "metadata": { 258 | "collapsed": false 259 | }, 260 | "outputs": [ 261 | { 262 | "data": { 263 | "text/plain": [ 264 | "True" 265 | ] 266 | }, 267 | "execution_count": 33, 268 | "metadata": {}, 269 | "output_type": "execute_result" 270 | } 271 | ], 272 | "source": [ 273 | "1 <= 2" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 34, 279 | "metadata": { 280 | "collapsed": false 281 | }, 282 | "outputs": [ 283 | { 284 | "data": { 285 | "text/plain": [ 286 | "True" 287 | ] 288 | }, 289 | "execution_count": 34, 290 | "metadata": {}, 291 | "output_type": "execute_result" 292 | } 293 | ], 294 | "source": [ 295 | "1 != 2" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 35, 301 | "metadata": { 302 | "collapsed": false 303 | }, 304 | "outputs": [ 305 | { 306 | "data": { 307 | "text/plain": [ 308 | "True" 309 | ] 310 | }, 311 | "execution_count": 35, 312 | "metadata": {}, 313 | "output_type": "execute_result" 314 | } 315 | ], 316 | "source": [ 317 | "1 >= 0" 318 | ] 319 | }, 320 | { 321 | "cell_type": "code", 322 | "execution_count": 38, 323 | "metadata": { 324 | "collapsed": false 325 | }, 326 | "outputs": [ 327 | { 328 | "data": { 329 | "text/plain": [ 330 | "True" 331 | ] 332 | }, 333 | "execution_count": 38, 334 | "metadata": {}, 335 | "output_type": "execute_result" 336 | } 337 | ], 338 | "source": [ 339 | "[1,1,1] == [1,1,1]" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": null, 345 | "metadata": { 346 | "collapsed": true 347 | }, 348 | "outputs": [], 349 | "source": [] 350 | } 351 | ], 352 | "metadata": { 353 | "kernelspec": { 354 | "display_name": "Python 3", 355 | "language": "python", 356 | "name": "python3" 357 | }, 358 | "language_info": { 359 | "codemirror_mode": { 360 | "name": "ipython", 361 | "version": 3 362 | }, 363 | "file_extension": ".py", 364 | "mimetype": "text/x-python", 365 | "name": "python", 366 | "nbconvert_exporter": "python", 367 | "pygments_lexer": "ipython3", 368 | "version": "3.5.0" 369 | } 370 | }, 371 | "nbformat": 4, 372 | "nbformat_minor": 0 373 | } 374 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Python Overview Part 3-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "cities = ['NY','LA','SF']" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "city = cities[0]" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "collapsed": false 30 | }, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "'NY'" 36 | ] 37 | }, 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "output_type": "execute_result" 41 | } 42 | ], 43 | "source": [ 44 | "city" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 12, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "name": "stdout", 56 | "output_type": "stream", 57 | "text": [ 58 | "Party\n", 59 | "It's hot here\n", 60 | "Where am I?\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "for city in cities:\n", 66 | " if city == 'NY':\n", 67 | " print(\"Party\")\n", 68 | " elif city == 'LA':\n", 69 | " print(\"It's hot here\")\n", 70 | " else:\n", 71 | " print('Where am I?')" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 8, 77 | "metadata": { 78 | "collapsed": true 79 | }, 80 | "outputs": [], 81 | "source": [ 82 | "city = cities[2]" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 13, 88 | "metadata": { 89 | "collapsed": true 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "t = (1,2,3)" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 14, 99 | "metadata": { 100 | "collapsed": false 101 | }, 102 | "outputs": [ 103 | { 104 | "data": { 105 | "text/plain": [ 106 | "(1, 2, 3)" 107 | ] 108 | }, 109 | "execution_count": 14, 110 | "metadata": {}, 111 | "output_type": "execute_result" 112 | } 113 | ], 114 | "source": [ 115 | "t" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 15, 121 | "metadata": { 122 | "collapsed": false 123 | }, 124 | "outputs": [ 125 | { 126 | "ename": "AttributeError", 127 | "evalue": "'tuple' object has no attribute 'append'", 128 | "output_type": "error", 129 | "traceback": [ 130 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 131 | "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", 132 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 133 | "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "t.append(2)" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 16, 144 | "metadata": { 145 | "collapsed": true 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "my_list = [1,2,3]" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 17, 155 | "metadata": { 156 | "collapsed": true 157 | }, 158 | "outputs": [], 159 | "source": [ 160 | "my_dict = {'Joe':22,'Mike':12,}" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 18, 166 | "metadata": { 167 | "collapsed": false 168 | }, 169 | "outputs": [ 170 | { 171 | "data": { 172 | "text/plain": [ 173 | "22" 174 | ] 175 | }, 176 | "execution_count": 18, 177 | "metadata": {}, 178 | "output_type": "execute_result" 179 | } 180 | ], 181 | "source": [ 182 | "my_dict['Joe']" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": 19, 188 | "metadata": { 189 | "collapsed": false 190 | }, 191 | "outputs": [ 192 | { 193 | "data": { 194 | "text/plain": [ 195 | "2" 196 | ] 197 | }, 198 | "execution_count": 19, 199 | "metadata": {}, 200 | "output_type": "execute_result" 201 | } 202 | ], 203 | "source": [ 204 | "len(my_dict)" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 21, 210 | "metadata": { 211 | "collapsed": true 212 | }, 213 | "outputs": [], 214 | "source": [ 215 | "def adder(x,y):\n", 216 | " \"\"\" This function will add x and y together \"\"\"\n", 217 | " answer = x + y\n", 218 | " return answer" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 22, 224 | "metadata": { 225 | "collapsed": false 226 | }, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "15" 232 | ] 233 | }, 234 | "execution_count": 22, 235 | "metadata": {}, 236 | "output_type": "execute_result" 237 | } 238 | ], 239 | "source": [ 240 | "adder(5,10)" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": null, 246 | "metadata": { 247 | "collapsed": true 248 | }, 249 | "outputs": [], 250 | "source": [] 251 | } 252 | ], 253 | "metadata": { 254 | "kernelspec": { 255 | "display_name": "Python 3", 256 | "language": "python", 257 | "name": "python3" 258 | }, 259 | "language_info": { 260 | "codemirror_mode": { 261 | "name": "ipython", 262 | "version": 3 263 | }, 264 | "file_extension": ".py", 265 | "mimetype": "text/x-python", 266 | "name": "python", 267 | "nbconvert_exporter": "python", 268 | "pygments_lexer": "ipython3", 269 | "version": "3.5.0" 270 | } 271 | }, 272 | "nbformat": 4, 273 | "nbformat_minor": 0 274 | } 275 | -------------------------------------------------------------------------------- /Lec_28_test.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/m6jones/Course-Notes---Learning-Python-for-Data-Analysis-and-Visualization-by-Jose-Portilla/39dcca6cd155c2d91404750016545a27b6a43b21/Lec_28_test.xlsx -------------------------------------------------------------------------------- /Lecture 10 - Array Transposition.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 4, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "array([[ 0, 1, 2, 3, 4],\n", 25 | " [ 5, 6, 7, 8, 9],\n", 26 | " [10, 11, 12, 13, 14],\n", 27 | " [15, 16, 17, 18, 19],\n", 28 | " [20, 21, 22, 23, 24],\n", 29 | " [25, 26, 27, 28, 29],\n", 30 | " [30, 31, 32, 33, 34],\n", 31 | " [35, 36, 37, 38, 39],\n", 32 | " [40, 41, 42, 43, 44],\n", 33 | " [45, 46, 47, 48, 49]])" 34 | ] 35 | }, 36 | "execution_count": 4, 37 | "metadata": {}, 38 | "output_type": "execute_result" 39 | } 40 | ], 41 | "source": [ 42 | "arr = np.arange(50).reshape((10,5))\n", 43 | "arr" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 5, 49 | "metadata": { 50 | "collapsed": false 51 | }, 52 | "outputs": [ 53 | { 54 | "data": { 55 | "text/plain": [ 56 | "array([[ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45],\n", 57 | " [ 1, 6, 11, 16, 21, 26, 31, 36, 41, 46],\n", 58 | " [ 2, 7, 12, 17, 22, 27, 32, 37, 42, 47],\n", 59 | " [ 3, 8, 13, 18, 23, 28, 33, 38, 43, 48],\n", 60 | " [ 4, 9, 14, 19, 24, 29, 34, 39, 44, 49]])" 61 | ] 62 | }, 63 | "execution_count": 5, 64 | "metadata": {}, 65 | "output_type": "execute_result" 66 | } 67 | ], 68 | "source": [ 69 | "arr.T" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 6, 75 | "metadata": { 76 | "collapsed": false 77 | }, 78 | "outputs": [ 79 | { 80 | "data": { 81 | "text/plain": [ 82 | "array([[7125, 7350, 7575, 7800, 8025],\n", 83 | " [7350, 7585, 7820, 8055, 8290],\n", 84 | " [7575, 7820, 8065, 8310, 8555],\n", 85 | " [7800, 8055, 8310, 8565, 8820],\n", 86 | " [8025, 8290, 8555, 8820, 9085]])" 87 | ] 88 | }, 89 | "execution_count": 6, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | } 93 | ], 94 | "source": [ 95 | "np.dot(arr.T, arr)" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 7, 101 | "metadata": { 102 | "collapsed": false 103 | }, 104 | "outputs": [ 105 | { 106 | "data": { 107 | "text/plain": [ 108 | "array([[[ 0, 1],\n", 109 | " [ 2, 3],\n", 110 | " [ 4, 5],\n", 111 | " [ 6, 7],\n", 112 | " [ 8, 9]],\n", 113 | "\n", 114 | " [[10, 11],\n", 115 | " [12, 13],\n", 116 | " [14, 15],\n", 117 | " [16, 17],\n", 118 | " [18, 19]],\n", 119 | "\n", 120 | " [[20, 21],\n", 121 | " [22, 23],\n", 122 | " [24, 25],\n", 123 | " [26, 27],\n", 124 | " [28, 29]],\n", 125 | "\n", 126 | " [[30, 31],\n", 127 | " [32, 33],\n", 128 | " [34, 35],\n", 129 | " [36, 37],\n", 130 | " [38, 39]],\n", 131 | "\n", 132 | " [[40, 41],\n", 133 | " [42, 43],\n", 134 | " [44, 45],\n", 135 | " [46, 47],\n", 136 | " [48, 49]]])" 137 | ] 138 | }, 139 | "execution_count": 7, 140 | "metadata": {}, 141 | "output_type": "execute_result" 142 | } 143 | ], 144 | "source": [ 145 | "arr3d = np.arange(50).reshape((5,5,2))\n", 146 | "arr3d" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 10, 152 | "metadata": { 153 | "collapsed": false 154 | }, 155 | "outputs": [ 156 | { 157 | "data": { 158 | "text/plain": [ 159 | "array([[[ 0, 1],\n", 160 | " [ 2, 3],\n", 161 | " [ 4, 5],\n", 162 | " [ 6, 7],\n", 163 | " [ 8, 9]],\n", 164 | "\n", 165 | " [[10, 11],\n", 166 | " [12, 13],\n", 167 | " [14, 15],\n", 168 | " [16, 17],\n", 169 | " [18, 19]],\n", 170 | "\n", 171 | " [[20, 21],\n", 172 | " [22, 23],\n", 173 | " [24, 25],\n", 174 | " [26, 27],\n", 175 | " [28, 29]],\n", 176 | "\n", 177 | " [[30, 31],\n", 178 | " [32, 33],\n", 179 | " [34, 35],\n", 180 | " [36, 37],\n", 181 | " [38, 39]],\n", 182 | "\n", 183 | " [[40, 41],\n", 184 | " [42, 43],\n", 185 | " [44, 45],\n", 186 | " [46, 47],\n", 187 | " [48, 49]]])" 188 | ] 189 | }, 190 | "execution_count": 10, 191 | "metadata": {}, 192 | "output_type": "execute_result" 193 | } 194 | ], 195 | "source": [ 196 | "arr3d.transpose((1,0,2))" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 11, 202 | "metadata": { 203 | "collapsed": false 204 | }, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "text/plain": [ 209 | "array([[1, 2, 3]])" 210 | ] 211 | }, 212 | "execution_count": 11, 213 | "metadata": {}, 214 | "output_type": "execute_result" 215 | } 216 | ], 217 | "source": [ 218 | "arr = np.array([[1,2,3]])\n", 219 | "arr" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": 13, 225 | "metadata": { 226 | "collapsed": false 227 | }, 228 | "outputs": [ 229 | { 230 | "data": { 231 | "text/plain": [ 232 | "array([[1],\n", 233 | " [2],\n", 234 | " [3]])" 235 | ] 236 | }, 237 | "execution_count": 13, 238 | "metadata": {}, 239 | "output_type": "execute_result" 240 | } 241 | ], 242 | "source": [ 243 | "arr.swapaxes(0,1)" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": null, 249 | "metadata": { 250 | "collapsed": true 251 | }, 252 | "outputs": [], 253 | "source": [] 254 | } 255 | ], 256 | "metadata": { 257 | "kernelspec": { 258 | "display_name": "Python 3", 259 | "language": "python", 260 | "name": "python3" 261 | }, 262 | "language_info": { 263 | "codemirror_mode": { 264 | "name": "ipython", 265 | "version": 3 266 | }, 267 | "file_extension": ".py", 268 | "mimetype": "text/x-python", 269 | "name": "python", 270 | "nbconvert_exporter": "python", 271 | "pygments_lexer": "ipython3", 272 | "version": "3.5.0" 273 | } 274 | }, 275 | "nbformat": 4, 276 | "nbformat_minor": 0 277 | } 278 | -------------------------------------------------------------------------------- /Lecture 11 - Universal Array Functions.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" 25 | ] 26 | }, 27 | "execution_count": 2, 28 | "metadata": {}, 29 | "output_type": "execute_result" 30 | } 31 | ], 32 | "source": [ 33 | "arr = np.arange(11)\n", 34 | "arr" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 3, 40 | "metadata": { 41 | "collapsed": false 42 | }, 43 | "outputs": [ 44 | { 45 | "data": { 46 | "text/plain": [ 47 | "array([ 0. , 1. , 1.41421356, 1.73205081, 2. ,\n", 48 | " 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ,\n", 49 | " 3.16227766])" 50 | ] 51 | }, 52 | "execution_count": 3, 53 | "metadata": {}, 54 | "output_type": "execute_result" 55 | } 56 | ], 57 | "source": [ 58 | "np.sqrt(arr)" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 4, 64 | "metadata": { 65 | "collapsed": false 66 | }, 67 | "outputs": [ 68 | { 69 | "data": { 70 | "text/plain": [ 71 | "array([ 1.00000000e+00, 2.71828183e+00, 7.38905610e+00,\n", 72 | " 2.00855369e+01, 5.45981500e+01, 1.48413159e+02,\n", 73 | " 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n", 74 | " 8.10308393e+03, 2.20264658e+04])" 75 | ] 76 | }, 77 | "execution_count": 4, 78 | "metadata": {}, 79 | "output_type": "execute_result" 80 | } 81 | ], 82 | "source": [ 83 | "np.exp(arr)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 5, 89 | "metadata": { 90 | "collapsed": false 91 | }, 92 | "outputs": [ 93 | { 94 | "data": { 95 | "text/plain": [ 96 | "array([ 0.56041841, 0.39410462, -1.40311281, 0.52177286, -0.88397746,\n", 97 | " 0.50731782, 1.48511633, -0.94075355, -0.22938291, 0.04752728])" 98 | ] 99 | }, 100 | "execution_count": 5, 101 | "metadata": {}, 102 | "output_type": "execute_result" 103 | } 104 | ], 105 | "source": [ 106 | "A = np.random.randn(10)\n", 107 | "A" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 6, 113 | "metadata": { 114 | "collapsed": false 115 | }, 116 | "outputs": [ 117 | { 118 | "data": { 119 | "text/plain": [ 120 | "array([-0.2517604 , 1.90173753, -1.13185127, 0.73174884, 0.46465893,\n", 121 | " -0.38132952, -0.11073532, 1.44856624, 0.92181173, 1.31479299])" 122 | ] 123 | }, 124 | "execution_count": 6, 125 | "metadata": {}, 126 | "output_type": "execute_result" 127 | } 128 | ], 129 | "source": [ 130 | "B = np.random.randn(10)\n", 131 | "B" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 8, 137 | "metadata": { 138 | "collapsed": false 139 | }, 140 | "outputs": [ 141 | { 142 | "data": { 143 | "text/plain": [ 144 | "array([ 0.30865802, 2.29584215, -2.53496407, 1.2535217 , -0.41931853,\n", 145 | " 0.1259883 , 1.37438101, 0.5078127 , 0.69242882, 1.36232026])" 146 | ] 147 | }, 148 | "execution_count": 8, 149 | "metadata": {}, 150 | "output_type": "execute_result" 151 | } 152 | ], 153 | "source": [ 154 | "#Binary Functions\n", 155 | "np.add(A,B)" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 9, 161 | "metadata": { 162 | "collapsed": false 163 | }, 164 | "outputs": [ 165 | { 166 | "data": { 167 | "text/plain": [ 168 | "array([ 0.56041841, 1.90173753, -1.13185127, 0.73174884, 0.46465893,\n", 169 | " 0.50731782, 1.48511633, 1.44856624, 0.92181173, 1.31479299])" 170 | ] 171 | }, 172 | "execution_count": 9, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "np.maximum(A,B)" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 10, 184 | "metadata": { 185 | "collapsed": false 186 | }, 187 | "outputs": [ 188 | { 189 | "data": { 190 | "text/plain": [ 191 | "True" 192 | ] 193 | }, 194 | "execution_count": 10, 195 | "metadata": {}, 196 | "output_type": "execute_result" 197 | } 198 | ], 199 | "source": [ 200 | "#Universal Functions on Arrays\n", 201 | "website = \"http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs\"\n", 202 | "import webbrowser\n", 203 | "webbrowser.open(website)" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": true 211 | }, 212 | "outputs": [], 213 | "source": [] 214 | } 215 | ], 216 | "metadata": { 217 | "kernelspec": { 218 | "display_name": "Python 3", 219 | "language": "python", 220 | "name": "python3" 221 | }, 222 | "language_info": { 223 | "codemirror_mode": { 224 | "name": "ipython", 225 | "version": 3 226 | }, 227 | "file_extension": ".py", 228 | "mimetype": "text/x-python", 229 | "name": "python", 230 | "nbconvert_exporter": "python", 231 | "pygments_lexer": "ipython3", 232 | "version": "3.5.0" 233 | } 234 | }, 235 | "nbformat": 4, 236 | "nbformat_minor": 0 237 | } 238 | -------------------------------------------------------------------------------- /Lecture 13 - Array Input and Output.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "arr = np.arange(5)" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "collapsed": false 30 | }, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "array([0, 1, 2, 3, 4])" 36 | ] 37 | }, 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "output_type": "execute_result" 41 | } 42 | ], 43 | "source": [ 44 | "arr" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 5, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "array([0, 1, 2, 3, 4])" 58 | ] 59 | }, 60 | "execution_count": 5, 61 | "metadata": {}, 62 | "output_type": "execute_result" 63 | } 64 | ], 65 | "source": [ 66 | "np.save('myarray',arr)\n", 67 | "arr" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 6, 73 | "metadata": { 74 | "collapsed": false 75 | }, 76 | "outputs": [ 77 | { 78 | "data": { 79 | "text/plain": [ 80 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 81 | ] 82 | }, 83 | "execution_count": 6, 84 | "metadata": {}, 85 | "output_type": "execute_result" 86 | } 87 | ], 88 | "source": [ 89 | "arr = np.arange(10)\n", 90 | "arr" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 7, 96 | "metadata": { 97 | "collapsed": false 98 | }, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "text/plain": [ 103 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 104 | ] 105 | }, 106 | "execution_count": 7, 107 | "metadata": {}, 108 | "output_type": "execute_result" 109 | } 110 | ], 111 | "source": [ 112 | "arr" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 8, 118 | "metadata": { 119 | "collapsed": false 120 | }, 121 | "outputs": [ 122 | { 123 | "data": { 124 | "text/plain": [ 125 | "array([0, 1, 2, 3, 4])" 126 | ] 127 | }, 128 | "execution_count": 8, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "np.load('myarray.npy')" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 11, 140 | "metadata": { 141 | "collapsed": false 142 | }, 143 | "outputs": [ 144 | { 145 | "data": { 146 | "text/plain": [ 147 | "array([0, 1, 2, 3, 4])" 148 | ] 149 | }, 150 | "execution_count": 11, 151 | "metadata": {}, 152 | "output_type": "execute_result" 153 | } 154 | ], 155 | "source": [ 156 | "arr1 = np.load('myarray.npy')\n", 157 | "arr1" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 12, 163 | "metadata": { 164 | "collapsed": true 165 | }, 166 | "outputs": [], 167 | "source": [ 168 | "arr2 = arr" 169 | ] 170 | }, 171 | { 172 | "cell_type": "code", 173 | "execution_count": 13, 174 | "metadata": { 175 | "collapsed": false 176 | }, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "text/plain": [ 181 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 182 | ] 183 | }, 184 | "execution_count": 13, 185 | "metadata": {}, 186 | "output_type": "execute_result" 187 | } 188 | ], 189 | "source": [ 190 | "arr2" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 14, 196 | "metadata": { 197 | "collapsed": true 198 | }, 199 | "outputs": [], 200 | "source": [ 201 | "np.savez('ziparray.npz',x=arr1,y=arr2)" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 15, 207 | "metadata": { 208 | "collapsed": true 209 | }, 210 | "outputs": [], 211 | "source": [ 212 | "archive_array = np.load('ziparray.npz')" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": 16, 218 | "metadata": { 219 | "collapsed": false 220 | }, 221 | "outputs": [ 222 | { 223 | "data": { 224 | "text/plain": [ 225 | "array([0, 1, 2, 3, 4])" 226 | ] 227 | }, 228 | "execution_count": 16, 229 | "metadata": {}, 230 | "output_type": "execute_result" 231 | } 232 | ], 233 | "source": [ 234 | "archive_array['x']" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 17, 240 | "metadata": { 241 | "collapsed": false 242 | }, 243 | "outputs": [ 244 | { 245 | "data": { 246 | "text/plain": [ 247 | "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" 248 | ] 249 | }, 250 | "execution_count": 17, 251 | "metadata": {}, 252 | "output_type": "execute_result" 253 | } 254 | ], 255 | "source": [ 256 | "archive_array['y']" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": 19, 262 | "metadata": { 263 | "collapsed": false 264 | }, 265 | "outputs": [ 266 | { 267 | "data": { 268 | "text/plain": [ 269 | "array([[1, 2, 3],\n", 270 | " [4, 5, 6]])" 271 | ] 272 | }, 273 | "execution_count": 19, 274 | "metadata": {}, 275 | "output_type": "execute_result" 276 | } 277 | ], 278 | "source": [ 279 | "arr = np.array([[1,2,3],[4,5,6]])\n", 280 | "arr" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": 22, 286 | "metadata": { 287 | "collapsed": false 288 | }, 289 | "outputs": [], 290 | "source": [ 291 | "np.savetxt('mytextarray.txt',arr,delimiter=',')" 292 | ] 293 | }, 294 | { 295 | "cell_type": "code", 296 | "execution_count": 24, 297 | "metadata": { 298 | "collapsed": false 299 | }, 300 | "outputs": [ 301 | { 302 | "data": { 303 | "text/plain": [ 304 | "array([[ 1., 2., 3.],\n", 305 | " [ 4., 5., 6.]])" 306 | ] 307 | }, 308 | "execution_count": 24, 309 | "metadata": {}, 310 | "output_type": "execute_result" 311 | } 312 | ], 313 | "source": [ 314 | "arr = np.loadtxt('mytextarray.txt',delimiter=',')\n", 315 | "arr" 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": null, 321 | "metadata": { 322 | "collapsed": true 323 | }, 324 | "outputs": [], 325 | "source": [] 326 | } 327 | ], 328 | "metadata": { 329 | "kernelspec": { 330 | "display_name": "Python 3", 331 | "language": "python", 332 | "name": "python3" 333 | }, 334 | "language_info": { 335 | "codemirror_mode": { 336 | "name": "ipython", 337 | "version": 3 338 | }, 339 | "file_extension": ".py", 340 | "mimetype": "text/x-python", 341 | "name": "python", 342 | "nbconvert_exporter": "python", 343 | "pygments_lexer": "ipython3", 344 | "version": "3.5.0" 345 | } 346 | }, 347 | "nbformat": 4, 348 | "nbformat_minor": 0 349 | } 350 | -------------------------------------------------------------------------------- /Lecture 16 - Index Objects.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "from pandas import Series, DataFrame\n", 13 | "import pandas as pd" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/plain": [ 26 | "A 1\n", 27 | "B 2\n", 28 | "C 3\n", 29 | "D 4\n", 30 | "dtype: int64" 31 | ] 32 | }, 33 | "execution_count": 3, 34 | "metadata": {}, 35 | "output_type": "execute_result" 36 | } 37 | ], 38 | "source": [ 39 | "my_ser = Series([1,2,3,4],index=['A','B','C','D'])\n", 40 | "my_ser" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 4, 46 | "metadata": { 47 | "collapsed": true 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "my_index = my_ser.index" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 5, 57 | "metadata": { 58 | "collapsed": false 59 | }, 60 | "outputs": [ 61 | { 62 | "data": { 63 | "text/plain": [ 64 | "Index(['A', 'B', 'C', 'D'], dtype='object')" 65 | ] 66 | }, 67 | "execution_count": 5, 68 | "metadata": {}, 69 | "output_type": "execute_result" 70 | } 71 | ], 72 | "source": [ 73 | "my_index" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 6, 79 | "metadata": { 80 | "collapsed": false 81 | }, 82 | "outputs": [ 83 | { 84 | "data": { 85 | "text/plain": [ 86 | "'C'" 87 | ] 88 | }, 89 | "execution_count": 6, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | } 93 | ], 94 | "source": [ 95 | "my_index[2]" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 8, 101 | "metadata": { 102 | "collapsed": false 103 | }, 104 | "outputs": [ 105 | { 106 | "data": { 107 | "text/plain": [ 108 | "Index(['C', 'D'], dtype='object')" 109 | ] 110 | }, 111 | "execution_count": 8, 112 | "metadata": {}, 113 | "output_type": "execute_result" 114 | } 115 | ], 116 | "source": [ 117 | "my_index[2:]" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 9, 123 | "metadata": { 124 | "collapsed": false 125 | }, 126 | "outputs": [ 127 | { 128 | "data": { 129 | "text/plain": [ 130 | "'A'" 131 | ] 132 | }, 133 | "execution_count": 9, 134 | "metadata": {}, 135 | "output_type": "execute_result" 136 | } 137 | ], 138 | "source": [ 139 | "my_index[0]" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": 10, 145 | "metadata": { 146 | "collapsed": false 147 | }, 148 | "outputs": [ 149 | { 150 | "ename": "TypeError", 151 | "evalue": "Index does not support mutable operations", 152 | "output_type": "error", 153 | "traceback": [ 154 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 155 | "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", 156 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_index\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Z'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 157 | "\u001b[1;32mD:\\Users\\Matt\\Anaconda3\\lib\\site-packages\\pandas\\core\\index.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 1122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1123\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1124\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Index does not support mutable operations\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1125\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 158 | "\u001b[1;31mTypeError\u001b[0m: Index does not support mutable operations" 159 | ] 160 | } 161 | ], 162 | "source": [ 163 | "my_index[0] = 'Z' #You cannot change them. They are const." 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": null, 169 | "metadata": { 170 | "collapsed": true 171 | }, 172 | "outputs": [], 173 | "source": [] 174 | } 175 | ], 176 | "metadata": { 177 | "kernelspec": { 178 | "display_name": "Python 3", 179 | "language": "python", 180 | "name": "python3" 181 | }, 182 | "language_info": { 183 | "codemirror_mode": { 184 | "name": "ipython", 185 | "version": 3 186 | }, 187 | "file_extension": ".py", 188 | "mimetype": "text/x-python", 189 | "name": "python", 190 | "nbconvert_exporter": "python", 191 | "pygments_lexer": "ipython3", 192 | "version": "3.5.0" 193 | } 194 | }, 195 | "nbformat": 4, 196 | "nbformat_minor": 0 197 | } 198 | -------------------------------------------------------------------------------- /Lecture 21 - Rank and Sort.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "\n", 14 | "from pandas import Series,DataFrame" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 3, 20 | "metadata": { 21 | "collapsed": false 22 | }, 23 | "outputs": [ 24 | { 25 | "data": { 26 | "text/plain": [ 27 | "C 0\n", 28 | "A 1\n", 29 | "B 2\n", 30 | "dtype: int64" 31 | ] 32 | }, 33 | "execution_count": 3, 34 | "metadata": {}, 35 | "output_type": "execute_result" 36 | } 37 | ], 38 | "source": [ 39 | "ser1 = Series(list(range(3)),index=['C','A','B'])\n", 40 | "ser1" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 4, 46 | "metadata": { 47 | "collapsed": false 48 | }, 49 | "outputs": [ 50 | { 51 | "data": { 52 | "text/plain": [ 53 | "A 1\n", 54 | "B 2\n", 55 | "C 0\n", 56 | "dtype: int64" 57 | ] 58 | }, 59 | "execution_count": 4, 60 | "metadata": {}, 61 | "output_type": "execute_result" 62 | } 63 | ], 64 | "source": [ 65 | "ser1.sort_index()" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 6, 71 | "metadata": { 72 | "collapsed": false 73 | }, 74 | "outputs": [ 75 | { 76 | "data": { 77 | "text/plain": [ 78 | "C 0\n", 79 | "A 1\n", 80 | "B 2\n", 81 | "dtype: int64" 82 | ] 83 | }, 84 | "execution_count": 6, 85 | "metadata": {}, 86 | "output_type": "execute_result" 87 | } 88 | ], 89 | "source": [ 90 | "ser1.sort_values() #he used ser1.order" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 7, 96 | "metadata": { 97 | "collapsed": true 98 | }, 99 | "outputs": [], 100 | "source": [ 101 | "from numpy.random import randn" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 8, 107 | "metadata": { 108 | "collapsed": false 109 | }, 110 | "outputs": [ 111 | { 112 | "data": { 113 | "text/plain": [ 114 | "0 -0.588250\n", 115 | "1 0.467589\n", 116 | "2 0.833443\n", 117 | "3 -0.908241\n", 118 | "4 1.413211\n", 119 | "5 0.389749\n", 120 | "6 0.275788\n", 121 | "7 -0.478224\n", 122 | "8 0.953781\n", 123 | "9 1.873889\n", 124 | "dtype: float64" 125 | ] 126 | }, 127 | "execution_count": 8, 128 | "metadata": {}, 129 | "output_type": "execute_result" 130 | } 131 | ], 132 | "source": [ 133 | "ser2 = Series(randn(10))\n", 134 | "ser2" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 12, 140 | "metadata": { 141 | "collapsed": false 142 | }, 143 | "outputs": [ 144 | { 145 | "data": { 146 | "text/plain": [ 147 | "3 -0.908241\n", 148 | "0 -0.588250\n", 149 | "7 -0.478224\n", 150 | "6 0.275788\n", 151 | "5 0.389749\n", 152 | "1 0.467589\n", 153 | "2 0.833443\n", 154 | "8 0.953781\n", 155 | "4 1.413211\n", 156 | "9 1.873889\n", 157 | "dtype: float64" 158 | ] 159 | }, 160 | "execution_count": 12, 161 | "metadata": {}, 162 | "output_type": "execute_result" 163 | } 164 | ], 165 | "source": [ 166 | "ser2.sort_values(inplace=True)\n", 167 | "ser2" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 13, 173 | "metadata": { 174 | "collapsed": false 175 | }, 176 | "outputs": [ 177 | { 178 | "data": { 179 | "text/plain": [ 180 | "3 1\n", 181 | "0 2\n", 182 | "7 3\n", 183 | "6 4\n", 184 | "5 5\n", 185 | "1 6\n", 186 | "2 7\n", 187 | "8 8\n", 188 | "4 9\n", 189 | "9 10\n", 190 | "dtype: float64" 191 | ] 192 | }, 193 | "execution_count": 13, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "ser2.rank()" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 15, 205 | "metadata": { 206 | "collapsed": false 207 | }, 208 | "outputs": [ 209 | { 210 | "data": { 211 | "text/plain": [ 212 | "0 0.160484\n", 213 | "1 -0.019495\n", 214 | "2 -1.263092\n", 215 | "3 -0.072723\n", 216 | "4 -1.140240\n", 217 | "5 -1.543357\n", 218 | "6 0.516009\n", 219 | "7 -1.584327\n", 220 | "8 0.112761\n", 221 | "9 1.702406\n", 222 | "dtype: float64" 223 | ] 224 | }, 225 | "execution_count": 15, 226 | "metadata": {}, 227 | "output_type": "execute_result" 228 | } 229 | ], 230 | "source": [ 231 | "ser3 = Series(randn(10))\n", 232 | "ser3" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 16, 238 | "metadata": { 239 | "collapsed": false 240 | }, 241 | "outputs": [ 242 | { 243 | "data": { 244 | "text/plain": [ 245 | "0 8\n", 246 | "1 6\n", 247 | "2 3\n", 248 | "3 5\n", 249 | "4 4\n", 250 | "5 2\n", 251 | "6 9\n", 252 | "7 1\n", 253 | "8 7\n", 254 | "9 10\n", 255 | "dtype: float64" 256 | ] 257 | }, 258 | "execution_count": 16, 259 | "metadata": {}, 260 | "output_type": "execute_result" 261 | } 262 | ], 263 | "source": [ 264 | "ser3.rank()" 265 | ] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "execution_count": 17, 270 | "metadata": { 271 | "collapsed": false 272 | }, 273 | "outputs": [ 274 | { 275 | "data": { 276 | "text/plain": [ 277 | "7 1\n", 278 | "5 2\n", 279 | "2 3\n", 280 | "4 4\n", 281 | "3 5\n", 282 | "1 6\n", 283 | "8 7\n", 284 | "0 8\n", 285 | "6 9\n", 286 | "9 10\n", 287 | "dtype: float64" 288 | ] 289 | }, 290 | "execution_count": 17, 291 | "metadata": {}, 292 | "output_type": "execute_result" 293 | } 294 | ], 295 | "source": [ 296 | "ser3.sort_values(inplace=True)\n", 297 | "ser3.rank()" 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 18, 303 | "metadata": { 304 | "collapsed": false 305 | }, 306 | "outputs": [ 307 | { 308 | "data": { 309 | "text/plain": [ 310 | "7 -1.584327\n", 311 | "5 -1.543357\n", 312 | "2 -1.263092\n", 313 | "4 -1.140240\n", 314 | "3 -0.072723\n", 315 | "1 -0.019495\n", 316 | "8 0.112761\n", 317 | "0 0.160484\n", 318 | "6 0.516009\n", 319 | "9 1.702406\n", 320 | "dtype: float64" 321 | ] 322 | }, 323 | "execution_count": 18, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "ser3" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": { 336 | "collapsed": true 337 | }, 338 | "outputs": [], 339 | "source": [] 340 | } 341 | ], 342 | "metadata": { 343 | "kernelspec": { 344 | "display_name": "Python 3", 345 | "language": "python", 346 | "name": "python3" 347 | }, 348 | "language_info": { 349 | "codemirror_mode": { 350 | "name": "ipython", 351 | "version": 3 352 | }, 353 | "file_extension": ".py", 354 | "mimetype": "text/x-python", 355 | "name": "python", 356 | "nbconvert_exporter": "python", 357 | "pygments_lexer": "ipython3", 358 | "version": "3.5.0" 359 | } 360 | }, 361 | "nbformat": 4, 362 | "nbformat_minor": 0 363 | } 364 | -------------------------------------------------------------------------------- /Lecture 26 - JSON with Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series,DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "# Heres an example of what a JSON (JavaScript Object Notation) looks like:\n", 25 | "json_obj = \"\"\"\n", 26 | "{ \"zoo_animal\": \"Lion\",\n", 27 | " \"food\": [\"Meat\", \"Veggies\", \"Honey\"],\n", 28 | " \"fur\": \"Golden\",\n", 29 | " \"clothes\": null, \n", 30 | " \"diet\": [{\"zoo_animal\": \"Gazelle\", \"food\":\"grass\", \"fur\": \"Brown\"}]\n", 31 | "}\n", 32 | "\"\"\"" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 3, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "import json" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 4, 49 | "metadata": { 50 | "collapsed": true 51 | }, 52 | "outputs": [], 53 | "source": [ 54 | "data = json.loads(json_obj)" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 5, 60 | "metadata": { 61 | "collapsed": false 62 | }, 63 | "outputs": [ 64 | { 65 | "data": { 66 | "text/plain": [ 67 | "{'clothes': None,\n", 68 | " 'diet': [{'food': 'grass', 'fur': 'Brown', 'zoo_animal': 'Gazelle'}],\n", 69 | " 'food': ['Meat', 'Veggies', 'Honey'],\n", 70 | " 'fur': 'Golden',\n", 71 | " 'zoo_animal': 'Lion'}" 72 | ] 73 | }, 74 | "execution_count": 5, 75 | "metadata": {}, 76 | "output_type": "execute_result" 77 | } 78 | ], 79 | "source": [ 80 | "data" 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 6, 86 | "metadata": { 87 | "collapsed": false 88 | }, 89 | "outputs": [ 90 | { 91 | "data": { 92 | "text/plain": [ 93 | "'{\"food\": [\"Meat\", \"Veggies\", \"Honey\"], \"clothes\": null, \"zoo_animal\": \"Lion\", \"fur\": \"Golden\", \"diet\": [{\"food\": \"grass\", \"zoo_animal\": \"Gazelle\", \"fur\": \"Brown\"}]}'" 94 | ] 95 | }, 96 | "execution_count": 6, 97 | "metadata": {}, 98 | "output_type": "execute_result" 99 | } 100 | ], 101 | "source": [ 102 | "json.dumps(data)" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 7, 108 | "metadata": { 109 | "collapsed": false 110 | }, 111 | "outputs": [ 112 | { 113 | "data": { 114 | "text/html": [ 115 | "
\n", 116 | "\n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | "
foodfurzoo_animal
0grassBrownGazelle
\n", 134 | "
" 135 | ], 136 | "text/plain": [ 137 | " food fur zoo_animal\n", 138 | "0 grass Brown Gazelle" 139 | ] 140 | }, 141 | "execution_count": 7, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "dframe = DataFrame(data['diet'])\n", 148 | "dframe" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": null, 154 | "metadata": { 155 | "collapsed": true 156 | }, 157 | "outputs": [], 158 | "source": [] 159 | } 160 | ], 161 | "metadata": { 162 | "kernelspec": { 163 | "display_name": "Python 3", 164 | "language": "python", 165 | "name": "python3" 166 | }, 167 | "language_info": { 168 | "codemirror_mode": { 169 | "name": "ipython", 170 | "version": 3 171 | }, 172 | "file_extension": ".py", 173 | "mimetype": "text/x-python", 174 | "name": "python", 175 | "nbconvert_exporter": "python", 176 | "pygments_lexer": "ipython3", 177 | "version": "3.5.0" 178 | } 179 | }, 180 | "nbformat": 4, 181 | "nbformat_minor": 0 182 | } 183 | -------------------------------------------------------------------------------- /Lecture 28 - Excel with Python.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "# pip install xlrd\n", 23 | "# pip install openpyxl" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 6, 29 | "metadata": { 30 | "collapsed": false 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "xlsfile = pd.ExcelFile('Lec_28_test.xlsx')" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 7, 40 | "metadata": { 41 | "collapsed": true 42 | }, 43 | "outputs": [], 44 | "source": [ 45 | "dframe = xlsfile.parse('Sheet1')" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 9, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/html": [ 58 | "
\n", 59 | "\n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | "
This is a testUnnamed: 1Unnamed: 2
0236678456
1234679456
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454564365
\n", 101 | "
" 102 | ], 103 | "text/plain": [ 104 | " This is a test Unnamed: 1 Unnamed: 2\n", 105 | "0 23 6678 456\n", 106 | "1 234 679 456\n", 107 | "2 234 7 345\n", 108 | "3 34 56 234\n", 109 | "4 5 456 4365" 110 | ] 111 | }, 112 | "execution_count": 9, 113 | "metadata": {}, 114 | "output_type": "execute_result" 115 | } 116 | ], 117 | "source": [ 118 | "dframe" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": { 125 | "collapsed": true 126 | }, 127 | "outputs": [], 128 | "source": [ 129 | "#Check documentation for more functionality" 130 | ] 131 | } 132 | ], 133 | "metadata": { 134 | "kernelspec": { 135 | "display_name": "Python 3", 136 | "language": "python", 137 | "name": "python3" 138 | }, 139 | "language_info": { 140 | "codemirror_mode": { 141 | "name": "ipython", 142 | "version": 3 143 | }, 144 | "file_extension": ".py", 145 | "mimetype": "text/x-python", 146 | "name": "python", 147 | "nbconvert_exporter": "python", 148 | "pygments_lexer": "ipython3", 149 | "version": "3.5.0" 150 | } 151 | }, 152 | "nbformat": 4, 153 | "nbformat_minor": 0 154 | } 155 | -------------------------------------------------------------------------------- /Lecture 35 - Duplicates in DataFrames.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series,DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/html": [ 26 | "
\n", 27 | "\n", 28 | " \n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | "
key1key2
0A2
1A2
2B2
3B3
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" 64 | ], 65 | "text/plain": [ 66 | " key1 key2\n", 67 | "0 A 2\n", 68 | "1 A 2\n", 69 | "2 B 2\n", 70 | "3 B 3\n", 71 | "4 B 3" 72 | ] 73 | }, 74 | "execution_count": 3, 75 | "metadata": {}, 76 | "output_type": "execute_result" 77 | } 78 | ], 79 | "source": [ 80 | "dframe = DataFrame({'key1': ['A'] * 2 + ['B'] * 3,\n", 81 | " 'key2': [2, 2, 2, 3, 3]})\n", 82 | "dframe" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 5, 88 | "metadata": { 89 | "collapsed": false 90 | }, 91 | "outputs": [ 92 | { 93 | "data": { 94 | "text/plain": [ 95 | "0 False\n", 96 | "1 True\n", 97 | "2 False\n", 98 | "3 False\n", 99 | "4 True\n", 100 | "dtype: bool" 101 | ] 102 | }, 103 | "execution_count": 5, 104 | "metadata": {}, 105 | "output_type": "execute_result" 106 | } 107 | ], 108 | "source": [ 109 | "dframe.duplicated()" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 6, 115 | "metadata": { 116 | "collapsed": false 117 | }, 118 | "outputs": [ 119 | { 120 | "data": { 121 | "text/html": [ 122 | "
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" 200 | ], 201 | "text/plain": [ 202 | " key1 key2\n", 203 | "0 A 2\n", 204 | "2 B 2" 205 | ] 206 | }, 207 | "execution_count": 7, 208 | "metadata": {}, 209 | "output_type": "execute_result" 210 | } 211 | ], 212 | "source": [ 213 | "dframe.drop_duplicates(['key1'])" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 8, 219 | "metadata": { 220 | "collapsed": false 221 | }, 222 | "outputs": [ 223 | { 224 | "data": { 225 | "text/html": [ 226 | "
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" 264 | ], 265 | "text/plain": [ 266 | " key1 key2\n", 267 | "0 A 2\n", 268 | "1 A 2\n", 269 | "2 B 2\n", 270 | "3 B 3\n", 271 | "4 B 3" 272 | ] 273 | }, 274 | "execution_count": 8, 275 | "metadata": {}, 276 | "output_type": "execute_result" 277 | } 278 | ], 279 | "source": [ 280 | "dframe" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": 12, 286 | "metadata": { 287 | "collapsed": false 288 | }, 289 | "outputs": [ 290 | { 291 | "data": { 292 | "text/html": [ 293 | "
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key1key2
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" 316 | ], 317 | "text/plain": [ 318 | " key1 key2\n", 319 | "1 A 2\n", 320 | "4 B 3" 321 | ] 322 | }, 323 | "execution_count": 12, 324 | "metadata": {}, 325 | "output_type": "execute_result" 326 | } 327 | ], 328 | "source": [ 329 | "dframe.drop_duplicates(['key1'], keep=\"last\")" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": { 336 | "collapsed": true 337 | }, 338 | "outputs": [], 339 | "source": [] 340 | } 341 | ], 342 | "metadata": { 343 | "kernelspec": { 344 | "display_name": "Python 3", 345 | "language": "python", 346 | "name": "python3" 347 | }, 348 | "language_info": { 349 | "codemirror_mode": { 350 | "name": "ipython", 351 | "version": 3 352 | }, 353 | "file_extension": ".py", 354 | "mimetype": "text/x-python", 355 | "name": "python", 356 | "nbconvert_exporter": "python", 357 | "pygments_lexer": "ipython3", 358 | "version": "3.5.0" 359 | } 360 | }, 361 | "nbformat": 4, 362 | "nbformat_minor": 0 363 | } 364 | -------------------------------------------------------------------------------- /Lecture 36 - Mapping.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/html": [ 26 | "
\n", 27 | "\n", 28 | " \n", 29 | " \n", 30 | " \n", 31 | " \n", 32 | " \n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | "
altitudecity
03158Alma
13000Brian Head
22762Fox Park
\n", 53 | "
" 54 | ], 55 | "text/plain": [ 56 | " altitude city\n", 57 | "0 3158 Alma\n", 58 | "1 3000 Brian Head\n", 59 | "2 2762 Fox Park" 60 | ] 61 | }, 62 | "execution_count": 2, 63 | "metadata": {}, 64 | "output_type": "execute_result" 65 | } 66 | ], 67 | "source": [ 68 | "dframe = DataFrame({'city':['Alma','Brian Head','Fox Park'],\n", 69 | " 'altitude':[3158,3000,2762]})\n", 70 | "dframe" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 3, 76 | "metadata": { 77 | "collapsed": true 78 | }, 79 | "outputs": [], 80 | "source": [ 81 | "state_map = {'Alma':'Colorado','Brian Head':'Utah','Fox Park':'Wyoming'}" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 4, 87 | "metadata": { 88 | "collapsed": false 89 | }, 90 | "outputs": [ 91 | { 92 | "data": { 93 | "text/html": [ 94 | "
\n", 95 | "\n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | "
altitudecitystate
03158AlmaColorado
13000Brian HeadUtah
22762Fox ParkWyoming
\n", 125 | "
" 126 | ], 127 | "text/plain": [ 128 | " altitude city state\n", 129 | "0 3158 Alma Colorado\n", 130 | "1 3000 Brian Head Utah\n", 131 | "2 2762 Fox Park Wyoming" 132 | ] 133 | }, 134 | "execution_count": 4, 135 | "metadata": {}, 136 | "output_type": "execute_result" 137 | } 138 | ], 139 | "source": [ 140 | "dframe['state'] = dframe['city'].map(state_map)\n", 141 | "dframe" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": null, 147 | "metadata": { 148 | "collapsed": true 149 | }, 150 | "outputs": [], 151 | "source": [] 152 | } 153 | ], 154 | "metadata": { 155 | "kernelspec": { 156 | "display_name": "Python 3", 157 | "language": "python", 158 | "name": "python3" 159 | }, 160 | "language_info": { 161 | "codemirror_mode": { 162 | "name": "ipython", 163 | "version": 3 164 | }, 165 | "file_extension": ".py", 166 | "mimetype": "text/x-python", 167 | "name": "python", 168 | "nbconvert_exporter": "python", 169 | "pygments_lexer": "ipython3", 170 | "version": "3.5.0" 171 | } 172 | }, 173 | "nbformat": 4, 174 | "nbformat_minor": 0 175 | } 176 | -------------------------------------------------------------------------------- /Lecture 37 - Replace.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "data": { 25 | "text/plain": [ 26 | "0 1\n", 27 | "1 2\n", 28 | "2 3\n", 29 | "3 4\n", 30 | "4 1\n", 31 | "5 2\n", 32 | "6 3\n", 33 | "7 4\n", 34 | "dtype: int64" 35 | ] 36 | }, 37 | "execution_count": 2, 38 | "metadata": {}, 39 | "output_type": "execute_result" 40 | } 41 | ], 42 | "source": [ 43 | "ser1 = Series([1,2,3,4,1,2,3,4])\n", 44 | "ser1" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "data": { 56 | "text/plain": [ 57 | "0 NaN\n", 58 | "1 2\n", 59 | "2 3\n", 60 | "3 4\n", 61 | "4 NaN\n", 62 | "5 2\n", 63 | "6 3\n", 64 | "7 4\n", 65 | "dtype: float64" 66 | ] 67 | }, 68 | "execution_count": 3, 69 | "metadata": {}, 70 | "output_type": "execute_result" 71 | } 72 | ], 73 | "source": [ 74 | "ser1.replace(1,np.nan)" 75 | ] 76 | }, 77 | { 78 | "cell_type": "code", 79 | "execution_count": 4, 80 | "metadata": { 81 | "collapsed": false 82 | }, 83 | "outputs": [ 84 | { 85 | "data": { 86 | "text/plain": [ 87 | "0 100\n", 88 | "1 2\n", 89 | "2 3\n", 90 | "3 400\n", 91 | "4 100\n", 92 | "5 2\n", 93 | "6 3\n", 94 | "7 400\n", 95 | "dtype: int64" 96 | ] 97 | }, 98 | "execution_count": 4, 99 | "metadata": {}, 100 | "output_type": "execute_result" 101 | } 102 | ], 103 | "source": [ 104 | "ser1.replace([1,4],[100,400])" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 5, 110 | "metadata": { 111 | "collapsed": false 112 | }, 113 | "outputs": [ 114 | { 115 | "data": { 116 | "text/plain": [ 117 | "0 1\n", 118 | "1 2\n", 119 | "2 3\n", 120 | "3 NaN\n", 121 | "4 1\n", 122 | "5 2\n", 123 | "6 3\n", 124 | "7 NaN\n", 125 | "dtype: float64" 126 | ] 127 | }, 128 | "execution_count": 5, 129 | "metadata": {}, 130 | "output_type": "execute_result" 131 | } 132 | ], 133 | "source": [ 134 | "ser1.replace({4:np.nan})" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": null, 140 | "metadata": { 141 | "collapsed": true 142 | }, 143 | "outputs": [], 144 | "source": [] 145 | } 146 | ], 147 | "metadata": { 148 | "kernelspec": { 149 | "display_name": "Python 3", 150 | "language": "python", 151 | "name": "python3" 152 | }, 153 | "language_info": { 154 | "codemirror_mode": { 155 | "name": "ipython", 156 | "version": 3 157 | }, 158 | "file_extension": ".py", 159 | "mimetype": "text/x-python", 160 | "name": "python", 161 | "nbconvert_exporter": "python", 162 | "pygments_lexer": "ipython3", 163 | "version": "3.5.0" 164 | } 165 | }, 166 | "nbformat": 4, 167 | "nbformat_minor": 0 168 | } 169 | -------------------------------------------------------------------------------- /Lecture 39 - Binning.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "years = [1990,1991,1992,2008,2012,2015,1987,1969,2013,2008,1999]" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 3, 30 | "metadata": { 31 | "collapsed": true 32 | }, 33 | "outputs": [], 34 | "source": [ 35 | "decade_bins = [1960,1970,1980,1990,2000,2010,2020]" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 4, 41 | "metadata": { 42 | "collapsed": true 43 | }, 44 | "outputs": [], 45 | "source": [ 46 | "decade_cat = pd.cut(years,decade_bins)" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 5, 52 | "metadata": { 53 | "collapsed": false 54 | }, 55 | "outputs": [ 56 | { 57 | "data": { 58 | "text/plain": [ 59 | "[(1980, 1990], (1990, 2000], (1990, 2000], (2000, 2010], (2010, 2020], ..., (1980, 1990], (1960, 1970], (2010, 2020], (2000, 2010], (1990, 2000]]\n", 60 | "Length: 11\n", 61 | "Categories (6, object): [(1960, 1970] < (1970, 1980] < (1980, 1990] < (1990, 2000] < (2000, 2010] < (2010, 2020]]" 62 | ] 63 | }, 64 | "execution_count": 5, 65 | "metadata": {}, 66 | "output_type": "execute_result" 67 | } 68 | ], 69 | "source": [ 70 | "decade_cat" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 6, 76 | "metadata": { 77 | "collapsed": false 78 | }, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/plain": [ 83 | "Index(['(1960, 1970]', '(1970, 1980]', '(1980, 1990]', '(1990, 2000]',\n", 84 | " '(2000, 2010]', '(2010, 2020]'],\n", 85 | " dtype='object')" 86 | ] 87 | }, 88 | "execution_count": 6, 89 | "metadata": {}, 90 | "output_type": "execute_result" 91 | } 92 | ], 93 | "source": [ 94 | "decade_cat.categories" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 7, 100 | "metadata": { 101 | "collapsed": false 102 | }, 103 | "outputs": [ 104 | { 105 | "data": { 106 | "text/plain": [ 107 | "(2010, 2020] 3\n", 108 | "(1990, 2000] 3\n", 109 | "(2000, 2010] 2\n", 110 | "(1980, 1990] 2\n", 111 | "(1960, 1970] 1\n", 112 | "(1970, 1980] 0\n", 113 | "dtype: int64" 114 | ] 115 | }, 116 | "execution_count": 7, 117 | "metadata": {}, 118 | "output_type": "execute_result" 119 | } 120 | ], 121 | "source": [ 122 | "pd.value_counts(decade_cat)" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 8, 128 | "metadata": { 129 | "collapsed": false 130 | }, 131 | "outputs": [ 132 | { 133 | "data": { 134 | "text/plain": [ 135 | "[(1969, 1992], (1969, 1992], (1969, 1992], (1992, 2015], (1992, 2015], ..., (1969, 1992], (1969, 1992], (1992, 2015], (1992, 2015], (1992, 2015]]\n", 136 | "Length: 11\n", 137 | "Categories (2, object): [(1969, 1992] < (1992, 2015]]" 138 | ] 139 | }, 140 | "execution_count": 8, 141 | "metadata": {}, 142 | "output_type": "execute_result" 143 | } 144 | ], 145 | "source": [ 146 | "pd.cut(years,2,precision=1)" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": null, 152 | "metadata": { 153 | "collapsed": true 154 | }, 155 | "outputs": [], 156 | "source": [] 157 | } 158 | ], 159 | "metadata": { 160 | "kernelspec": { 161 | "display_name": "Python 3", 162 | "language": "python", 163 | "name": "python3" 164 | }, 165 | "language_info": { 166 | "codemirror_mode": { 167 | "name": "ipython", 168 | "version": 3 169 | }, 170 | "file_extension": ".py", 171 | "mimetype": "text/x-python", 172 | "name": "python", 173 | "nbconvert_exporter": "python", 174 | "pygments_lexer": "ipython3", 175 | "version": "3.5.0" 176 | } 177 | }, 178 | "nbformat": 4, 179 | "nbformat_minor": 0 180 | } 181 | -------------------------------------------------------------------------------- /Lecture 41 - Permutation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from pandas import Series, DataFrame" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 3, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "dframe = DataFrame(np.arange(16).reshape((4, 4)))" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 4, 30 | "metadata": { 31 | "collapsed": true 32 | }, 33 | "outputs": [], 34 | "source": [ 35 | "blender = np.random.permutation(4)" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 5, 41 | "metadata": { 42 | "collapsed": false 43 | }, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/plain": [ 48 | "array([3, 2, 0, 1])" 49 | ] 50 | }, 51 | "execution_count": 5, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | } 55 | ], 56 | "source": [ 57 | "blender" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 6, 63 | "metadata": { 64 | "collapsed": false 65 | }, 66 | "outputs": [ 67 | { 68 | "data": { 69 | "text/html": [ 70 | "
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" 184 | ], 185 | "text/plain": [ 186 | " 0 1 2 3\n", 187 | "3 12 13 14 15\n", 188 | "2 8 9 10 11\n", 189 | "0 0 1 2 3\n", 190 | "1 4 5 6 7" 191 | ] 192 | }, 193 | "execution_count": 7, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "dframe.take(blender)" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 9, 205 | "metadata": { 206 | "collapsed": false 207 | }, 208 | "outputs": [], 209 | "source": [ 210 | "box = np.array([1,2,3])" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 10, 216 | "metadata": { 217 | "collapsed": true 218 | }, 219 | "outputs": [], 220 | "source": [ 221 | "shaker = np.random.randint(0,len(box),size=10)" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 11, 227 | "metadata": { 228 | "collapsed": false 229 | }, 230 | "outputs": [ 231 | { 232 | "data": { 233 | "text/plain": [ 234 | "array([2, 0, 2, 2, 1, 1, 0, 2, 0, 0])" 235 | ] 236 | }, 237 | "execution_count": 11, 238 | "metadata": {}, 239 | "output_type": "execute_result" 240 | } 241 | ], 242 | "source": [ 243 | "shaker" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 14, 249 | "metadata": { 250 | "collapsed": false 251 | }, 252 | "outputs": [ 253 | { 254 | "data": { 255 | "text/plain": [ 256 | "array([3, 1, 3, 3, 2, 2, 1, 3, 1, 1])" 257 | ] 258 | }, 259 | "execution_count": 14, 260 | "metadata": {}, 261 | "output_type": "execute_result" 262 | } 263 | ], 264 | "source": [ 265 | "hand_grabs = box.take(shaker)\n", 266 | "hand_grabs" 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": null, 272 | "metadata": { 273 | "collapsed": true 274 | }, 275 | "outputs": [], 276 | "source": [] 277 | } 278 | ], 279 | "metadata": { 280 | "kernelspec": { 281 | "display_name": "Python 3", 282 | "language": "python", 283 | "name": "python3" 284 | }, 285 | "language_info": { 286 | "codemirror_mode": { 287 | "name": "ipython", 288 | "version": 3 289 | }, 290 | "file_extension": ".py", 291 | "mimetype": "text/x-python", 292 | "name": "python", 293 | "nbconvert_exporter": "python", 294 | "pygments_lexer": "ipython3", 295 | "version": "3.5.0" 296 | } 297 | }, 298 | "nbformat": 4, 299 | "nbformat_minor": 0 300 | } 301 | -------------------------------------------------------------------------------- /Lecture 46 - Cross Tabulation.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import pandas as pd" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 4, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "from io import StringIO #Python 2 from StringIO import StringIO" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 5, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "data=\"\"\"\\\n", 34 | "Sample Animal Intelligence\n", 35 | "1 Dog Smart\n", 36 | "2 Dog Smart\n", 37 | "3 Cat Dumb\n", 38 | "4 Cat Dumb\n", 39 | "5 Dog Dumb\n", 40 | "6 Cat Smart\"\"\"" 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 6, 46 | "metadata": { 47 | "collapsed": true 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "dframe = pd.read_table(StringIO(data),sep='\\s+')" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 7, 57 | "metadata": { 58 | "collapsed": false 59 | }, 60 | "outputs": [ 61 | { 62 | "data": { 63 | "text/html": [ 64 | "
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SampleAnimalIntelligence
01DogSmart
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" 114 | ], 115 | "text/plain": [ 116 | " Sample Animal Intelligence\n", 117 | "0 1 Dog Smart\n", 118 | "1 2 Dog Smart\n", 119 | "2 3 Cat Dumb\n", 120 | "3 4 Cat Dumb\n", 121 | "4 5 Dog Dumb\n", 122 | "5 6 Cat Smart" 123 | ] 124 | }, 125 | "execution_count": 7, 126 | "metadata": {}, 127 | "output_type": "execute_result" 128 | } 129 | ], 130 | "source": [ 131 | "dframe" 132 | ] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "execution_count": 9, 137 | "metadata": { 138 | "collapsed": false 139 | }, 140 | "outputs": [ 141 | { 142 | "data": { 143 | "text/html": [ 144 | "
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IntelligenceDumbSmartAll
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" 182 | ], 183 | "text/plain": [ 184 | "Intelligence Dumb Smart All\n", 185 | "Animal \n", 186 | "Cat 2 1 3\n", 187 | "Dog 1 2 3\n", 188 | "All 3 3 6" 189 | ] 190 | }, 191 | "execution_count": 9, 192 | "metadata": {}, 193 | "output_type": "execute_result" 194 | } 195 | ], 196 | "source": [ 197 | "pd.crosstab(dframe.Animal,dframe.Intelligence, margins=True)" 198 | ] 199 | }, 200 | { 201 | "cell_type": "code", 202 | "execution_count": null, 203 | "metadata": { 204 | "collapsed": true 205 | }, 206 | "outputs": [], 207 | "source": [] 208 | } 209 | ], 210 | "metadata": { 211 | "kernelspec": { 212 | "display_name": "Python 3", 213 | "language": "python", 214 | "name": "python3" 215 | }, 216 | "language_info": { 217 | "codemirror_mode": { 218 | "name": "ipython", 219 | "version": 3 220 | }, 221 | "file_extension": ".py", 222 | "mimetype": "text/x-python", 223 | "name": "python", 224 | "nbconvert_exporter": "python", 225 | "pygments_lexer": "ipython3", 226 | "version": "3.5.0" 227 | } 228 | }, 229 | "nbformat": 4, 230 | "nbformat_minor": 0 231 | } 232 | -------------------------------------------------------------------------------- /Lecture 47 - Installing Seaborn.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "#http://stanford.edu/~mwaskom/software/seaborn/installing.html" 12 | ] 13 | } 14 | ], 15 | "metadata": { 16 | "kernelspec": { 17 | "display_name": "Python 3", 18 | "language": "python", 19 | "name": "python3" 20 | }, 21 | "language_info": { 22 | "codemirror_mode": { 23 | "name": "ipython", 24 | "version": 3 25 | }, 26 | "file_extension": ".py", 27 | "mimetype": "text/x-python", 28 | "name": "python", 29 | "nbconvert_exporter": "python", 30 | "pygments_lexer": "ipython3", 31 | "version": "3.5.0" 32 | } 33 | }, 34 | "nbformat": 4, 35 | "nbformat_minor": 0 36 | } 37 | -------------------------------------------------------------------------------- /Lecture 7 - Creating arrays.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "my_list1 = [1,2,3,4]" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "collapsed": true 30 | }, 31 | "outputs": [], 32 | "source": [ 33 | "my_array1 = np.array(my_list1)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 4, 39 | "metadata": { 40 | "collapsed": false 41 | }, 42 | "outputs": [ 43 | { 44 | "data": { 45 | "text/plain": [ 46 | "array([1, 2, 3, 4])" 47 | ] 48 | }, 49 | "execution_count": 4, 50 | "metadata": {}, 51 | "output_type": "execute_result" 52 | } 53 | ], 54 | "source": [ 55 | "my_array1" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 25, 61 | "metadata": { 62 | "collapsed": true 63 | }, 64 | "outputs": [], 65 | "source": [ 66 | "my_list2 = [11,22,33,44]" 67 | ] 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 26, 72 | "metadata": { 73 | "collapsed": false 74 | }, 75 | "outputs": [], 76 | "source": [ 77 | "my_lists = [my_list1, my_list2]" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 27, 83 | "metadata": { 84 | "collapsed": true 85 | }, 86 | "outputs": [], 87 | "source": [ 88 | "my_array2 = np.array(my_lists)" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 24, 94 | "metadata": { 95 | "collapsed": false 96 | }, 97 | "outputs": [ 98 | { 99 | "data": { 100 | "text/plain": [ 101 | "array([[1, 2, 3, 4], [11, 22, 33]], dtype=object)" 102 | ] 103 | }, 104 | "execution_count": 24, 105 | "metadata": {}, 106 | "output_type": "execute_result" 107 | } 108 | ], 109 | "source": [ 110 | "my_array2 #what happens if they are not the same length?" 111 | ] 112 | }, 113 | { 114 | "cell_type": "code", 115 | "execution_count": 10, 116 | "metadata": { 117 | "collapsed": false 118 | }, 119 | "outputs": [ 120 | { 121 | "data": { 122 | "text/plain": [ 123 | "(2, 4)" 124 | ] 125 | }, 126 | "execution_count": 10, 127 | "metadata": {}, 128 | "output_type": "execute_result" 129 | } 130 | ], 131 | "source": [ 132 | "my_array2.shape" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 11, 138 | "metadata": { 139 | "collapsed": false 140 | }, 141 | "outputs": [ 142 | { 143 | "data": { 144 | "text/plain": [ 145 | "dtype('int32')" 146 | ] 147 | }, 148 | "execution_count": 11, 149 | "metadata": {}, 150 | "output_type": "execute_result" 151 | } 152 | ], 153 | "source": [ 154 | "my_array2.dtype" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 13, 160 | "metadata": { 161 | "collapsed": false 162 | }, 163 | "outputs": [ 164 | { 165 | "data": { 166 | "text/plain": [ 167 | "array([ 0., 0., 0., 0., 0.])" 168 | ] 169 | }, 170 | "execution_count": 13, 171 | "metadata": {}, 172 | "output_type": "execute_result" 173 | } 174 | ], 175 | "source": [ 176 | "np.zeros(5)" 177 | ] 178 | }, 179 | { 180 | "cell_type": "code", 181 | "execution_count": 14, 182 | "metadata": { 183 | "collapsed": true 184 | }, 185 | "outputs": [], 186 | "source": [ 187 | "my_zeros_array = np.zeros(5)" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 15, 193 | "metadata": { 194 | "collapsed": false 195 | }, 196 | "outputs": [ 197 | { 198 | "data": { 199 | "text/plain": [ 200 | "dtype('float64')" 201 | ] 202 | }, 203 | "execution_count": 15, 204 | "metadata": {}, 205 | "output_type": "execute_result" 206 | } 207 | ], 208 | "source": [ 209 | "my_zeros_array.dtype" 210 | ] 211 | }, 212 | { 213 | "cell_type": "code", 214 | "execution_count": 16, 215 | "metadata": { 216 | "collapsed": false 217 | }, 218 | "outputs": [ 219 | { 220 | "data": { 221 | "text/plain": [ 222 | "array([[ 1., 1., 1., 1., 1.],\n", 223 | " [ 1., 1., 1., 1., 1.],\n", 224 | " [ 1., 1., 1., 1., 1.],\n", 225 | " [ 1., 1., 1., 1., 1.],\n", 226 | " [ 1., 1., 1., 1., 1.]])" 227 | ] 228 | }, 229 | "execution_count": 16, 230 | "metadata": {}, 231 | "output_type": "execute_result" 232 | } 233 | ], 234 | "source": [ 235 | "np.ones([5,5])" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 17, 241 | "metadata": { 242 | "collapsed": false 243 | }, 244 | "outputs": [ 245 | { 246 | "data": { 247 | "text/plain": [ 248 | "array([ 0., 0., 0., 0., 0.])" 249 | ] 250 | }, 251 | "execution_count": 17, 252 | "metadata": {}, 253 | "output_type": "execute_result" 254 | } 255 | ], 256 | "source": [ 257 | "np.empty(5)" 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": 18, 263 | "metadata": { 264 | "collapsed": false 265 | }, 266 | "outputs": [ 267 | { 268 | "data": { 269 | "text/plain": [ 270 | "array([[ 1., 0., 0., 0., 0.],\n", 271 | " [ 0., 1., 0., 0., 0.],\n", 272 | " [ 0., 0., 1., 0., 0.],\n", 273 | " [ 0., 0., 0., 1., 0.],\n", 274 | " [ 0., 0., 0., 0., 1.]])" 275 | ] 276 | }, 277 | "execution_count": 18, 278 | "metadata": {}, 279 | "output_type": "execute_result" 280 | } 281 | ], 282 | "source": [ 283 | "np.eye(5)" 284 | ] 285 | }, 286 | { 287 | "cell_type": "code", 288 | "execution_count": 19, 289 | "metadata": { 290 | "collapsed": false 291 | }, 292 | "outputs": [ 293 | { 294 | "data": { 295 | "text/plain": [ 296 | "array([0, 1, 2, 3, 4])" 297 | ] 298 | }, 299 | "execution_count": 19, 300 | "metadata": {}, 301 | "output_type": "execute_result" 302 | } 303 | ], 304 | "source": [ 305 | "np.arange(5)" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 20, 311 | "metadata": { 312 | "collapsed": false 313 | }, 314 | "outputs": [ 315 | { 316 | "data": { 317 | "text/plain": [ 318 | "array([ 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37,\n", 319 | " 39, 41, 43, 45, 47, 49])" 320 | ] 321 | }, 322 | "execution_count": 20, 323 | "metadata": {}, 324 | "output_type": "execute_result" 325 | } 326 | ], 327 | "source": [ 328 | "np.arange(5,50,2)" 329 | ] 330 | }, 331 | { 332 | "cell_type": "code", 333 | "execution_count": null, 334 | "metadata": { 335 | "collapsed": true 336 | }, 337 | "outputs": [], 338 | "source": [] 339 | } 340 | ], 341 | "metadata": { 342 | "kernelspec": { 343 | "display_name": "Python 3", 344 | "language": "python", 345 | "name": "python3" 346 | }, 347 | "language_info": { 348 | "codemirror_mode": { 349 | "name": "ipython", 350 | "version": 3 351 | }, 352 | "file_extension": ".py", 353 | "mimetype": "text/x-python", 354 | "name": "python", 355 | "nbconvert_exporter": "python", 356 | "pygments_lexer": "ipython3", 357 | "version": "3.5.0" 358 | } 359 | }, 360 | "nbformat": 4, 361 | "nbformat_minor": 0 362 | } 363 | -------------------------------------------------------------------------------- /Lecture 8 - Using arrays and scalars.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "data": { 23 | "text/plain": [ 24 | "2.5" 25 | ] 26 | }, 27 | "execution_count": 2, 28 | "metadata": {}, 29 | "output_type": "execute_result" 30 | } 31 | ], 32 | "source": [ 33 | "5/2" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 3, 39 | "metadata": { 40 | "collapsed": true 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "#For Python 2\n", 45 | "#from __future__ import division" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 4, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/plain": [ 58 | "2.5" 59 | ] 60 | }, 61 | "execution_count": 4, 62 | "metadata": {}, 63 | "output_type": "execute_result" 64 | } 65 | ], 66 | "source": [ 67 | "5/2" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 5, 73 | "metadata": { 74 | "collapsed": true 75 | }, 76 | "outputs": [], 77 | "source": [ 78 | "arr1 = np.array([[1,2,3,4],[8,9,10,11]])" 79 | ] 80 | }, 81 | { 82 | "cell_type": "code", 83 | "execution_count": 6, 84 | "metadata": { 85 | "collapsed": false 86 | }, 87 | "outputs": [ 88 | { 89 | "data": { 90 | "text/plain": [ 91 | "array([[ 1, 2, 3, 4],\n", 92 | " [ 8, 9, 10, 11]])" 93 | ] 94 | }, 95 | "execution_count": 6, 96 | "metadata": {}, 97 | "output_type": "execute_result" 98 | } 99 | ], 100 | "source": [ 101 | "arr1" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 7, 107 | "metadata": { 108 | "collapsed": false 109 | }, 110 | "outputs": [ 111 | { 112 | "data": { 113 | "text/plain": [ 114 | "array([[ 1, 4, 9, 16],\n", 115 | " [ 64, 81, 100, 121]])" 116 | ] 117 | }, 118 | "execution_count": 7, 119 | "metadata": {}, 120 | "output_type": "execute_result" 121 | } 122 | ], 123 | "source": [ 124 | "arr1*arr1" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 8, 130 | "metadata": { 131 | "collapsed": false 132 | }, 133 | "outputs": [ 134 | { 135 | "data": { 136 | "text/plain": [ 137 | "array([[0, 0, 0, 0],\n", 138 | " [0, 0, 0, 0]])" 139 | ] 140 | }, 141 | "execution_count": 8, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "arr1 - arr1" 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "execution_count": 9, 153 | "metadata": { 154 | "collapsed": false 155 | }, 156 | "outputs": [ 157 | { 158 | "data": { 159 | "text/plain": [ 160 | "array([[ 1. , 0.5 , 0.33333333, 0.25 ],\n", 161 | " [ 0.125 , 0.11111111, 0.1 , 0.09090909]])" 162 | ] 163 | }, 164 | "execution_count": 9, 165 | "metadata": {}, 166 | "output_type": "execute_result" 167 | } 168 | ], 169 | "source": [ 170 | "1 / arr1" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 13, 176 | "metadata": { 177 | "collapsed": false 178 | }, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "text/plain": [ 183 | "array([[ 1, 8, 27, 64],\n", 184 | " [ 512, 729, 1000, 1331]], dtype=int32)" 185 | ] 186 | }, 187 | "execution_count": 13, 188 | "metadata": {}, 189 | "output_type": "execute_result" 190 | } 191 | ], 192 | "source": [ 193 | "arr1**3" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": null, 199 | "metadata": { 200 | "collapsed": true 201 | }, 202 | "outputs": [], 203 | "source": [] 204 | } 205 | ], 206 | "metadata": { 207 | "kernelspec": { 208 | "display_name": "Python 3", 209 | "language": "python", 210 | "name": "python3" 211 | }, 212 | "language_info": { 213 | "codemirror_mode": { 214 | "name": "ipython", 215 | "version": 3 216 | }, 217 | "file_extension": ".py", 218 | "mimetype": "text/x-python", 219 | "name": "python", 220 | "nbconvert_exporter": "python", 221 | "pygments_lexer": "ipython3", 222 | "version": "3.5.0" 223 | } 224 | }, 225 | "nbformat": 4, 226 | "nbformat_minor": 0 227 | } 228 | -------------------------------------------------------------------------------- /Python Overview Part 2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 10, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "cities = ['NY','LA','SF']" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 11, 17 | "metadata": { 18 | "collapsed": false 19 | }, 20 | "outputs": [ 21 | { 22 | "name": "stdout", 23 | "output_type": "stream", 24 | "text": [ 25 | "NY\n", 26 | "LA\n", 27 | "SF\n" 28 | ] 29 | } 30 | ], 31 | "source": [ 32 | "for city in cities:\n", 33 | " print(city)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": 12, 39 | "metadata": { 40 | "collapsed": false 41 | }, 42 | "outputs": [ 43 | { 44 | "name": "stdout", 45 | "output_type": "stream", 46 | "text": [ 47 | "I love NY\n", 48 | "I love LA\n", 49 | "I love SF\n" 50 | ] 51 | } 52 | ], 53 | "source": [ 54 | "for city in cities:\n", 55 | " phrase = 'I love ' + city\n", 56 | " print(phrase)" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 13, 62 | "metadata": { 63 | "collapsed": false 64 | }, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "The inverse of 1 is 1.0\n", 71 | "The inverse of 2 is 0.5\n", 72 | "The inverse of 3 is 0.3333333333333333\n", 73 | "The inverse of 4 is 0.25\n", 74 | "The inverse of 5 is 0.2\n", 75 | "The inverse of 6 is 0.16666666666666666\n", 76 | "The inverse of 7 is 0.14285714285714285\n", 77 | "The inverse of 8 is 0.125\n", 78 | "The inverse of 9 is 0.1111111111111111\n" 79 | ] 80 | } 81 | ], 82 | "source": [ 83 | "for n in range(1,10):\n", 84 | " print('The inverse of',n, 'is',1/n) # in python 2 use 1.0/n" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": 14, 90 | "metadata": { 91 | "collapsed": false 92 | }, 93 | "outputs": [ 94 | { 95 | "name": "stdout", 96 | "output_type": "stream", 97 | "text": [ 98 | "H\n", 99 | "e\n", 100 | "l\n", 101 | "l\n", 102 | "o\n" 103 | ] 104 | } 105 | ], 106 | "source": [ 107 | "for letter in 'Hello':\n", 108 | " print(letter)" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 17, 114 | "metadata": { 115 | "collapsed": false 116 | }, 117 | "outputs": [ 118 | { 119 | "data": { 120 | "text/plain": [ 121 | "'NY'" 122 | ] 123 | }, 124 | "execution_count": 17, 125 | "metadata": {}, 126 | "output_type": "execute_result" 127 | } 128 | ], 129 | "source": [ 130 | "cities[0]" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 28, 136 | "metadata": { 137 | "collapsed": false 138 | }, 139 | "outputs": [ 140 | { 141 | "name": "stdout", 142 | "output_type": "stream", 143 | "text": [ 144 | "party!\n" 145 | ] 146 | } 147 | ], 148 | "source": [ 149 | "if city == 'NY':\n", 150 | " print('party!')\n", 151 | "else:\n", 152 | " print('Work')" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 25, 158 | "metadata": { 159 | "collapsed": false 160 | }, 161 | "outputs": [], 162 | "source": [ 163 | "city='NY' #Note the order doesn't matter ^-----" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 29, 169 | "metadata": { 170 | "collapsed": false 171 | }, 172 | "outputs": [ 173 | { 174 | "data": { 175 | "text/plain": [ 176 | "False" 177 | ] 178 | }, 179 | "execution_count": 29, 180 | "metadata": {}, 181 | "output_type": "execute_result" 182 | } 183 | ], 184 | "source": [ 185 | "1==2" 186 | ] 187 | }, 188 | { 189 | "cell_type": "code", 190 | "execution_count": 30, 191 | "metadata": { 192 | "collapsed": false 193 | }, 194 | "outputs": [ 195 | { 196 | "data": { 197 | "text/plain": [ 198 | "True" 199 | ] 200 | }, 201 | "execution_count": 30, 202 | "metadata": {}, 203 | "output_type": "execute_result" 204 | } 205 | ], 206 | "source": [ 207 | "2 == 2" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 31, 213 | "metadata": { 214 | "collapsed": false 215 | }, 216 | "outputs": [ 217 | { 218 | "data": { 219 | "text/plain": [ 220 | "False" 221 | ] 222 | }, 223 | "execution_count": 31, 224 | "metadata": {}, 225 | "output_type": "execute_result" 226 | } 227 | ], 228 | "source": [ 229 | "3 > 4" 230 | ] 231 | }, 232 | { 233 | "cell_type": "code", 234 | "execution_count": 32, 235 | "metadata": { 236 | "collapsed": false 237 | }, 238 | "outputs": [ 239 | { 240 | "data": { 241 | "text/plain": [ 242 | "True" 243 | ] 244 | }, 245 | "execution_count": 32, 246 | "metadata": {}, 247 | "output_type": "execute_result" 248 | } 249 | ], 250 | "source": [ 251 | "4 < 5" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 33, 257 | "metadata": { 258 | "collapsed": false 259 | }, 260 | "outputs": [ 261 | { 262 | "data": { 263 | "text/plain": [ 264 | "True" 265 | ] 266 | }, 267 | "execution_count": 33, 268 | "metadata": {}, 269 | "output_type": "execute_result" 270 | } 271 | ], 272 | "source": [ 273 | "1 <= 2" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 34, 279 | "metadata": { 280 | "collapsed": false 281 | }, 282 | "outputs": [ 283 | { 284 | "data": { 285 | "text/plain": [ 286 | "True" 287 | ] 288 | }, 289 | "execution_count": 34, 290 | "metadata": {}, 291 | "output_type": "execute_result" 292 | } 293 | ], 294 | "source": [ 295 | "1 != 2" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 35, 301 | "metadata": { 302 | "collapsed": false 303 | }, 304 | "outputs": [ 305 | { 306 | "data": { 307 | "text/plain": [ 308 | "True" 309 | ] 310 | }, 311 | "execution_count": 35, 312 | "metadata": {}, 313 | "output_type": "execute_result" 314 | } 315 | ], 316 | "source": [ 317 | "1 >= 0" 318 | ] 319 | }, 320 | { 321 | "cell_type": "code", 322 | "execution_count": 38, 323 | "metadata": { 324 | "collapsed": false 325 | }, 326 | "outputs": [ 327 | { 328 | "data": { 329 | "text/plain": [ 330 | "True" 331 | ] 332 | }, 333 | "execution_count": 38, 334 | "metadata": {}, 335 | "output_type": "execute_result" 336 | } 337 | ], 338 | "source": [ 339 | "[1,1,1] == [1,1,1]" 340 | ] 341 | }, 342 | { 343 | "cell_type": "code", 344 | "execution_count": null, 345 | "metadata": { 346 | "collapsed": true 347 | }, 348 | "outputs": [], 349 | "source": [] 350 | } 351 | ], 352 | "metadata": { 353 | "kernelspec": { 354 | "display_name": "Python 3", 355 | "language": "python", 356 | "name": "python3" 357 | }, 358 | "language_info": { 359 | "codemirror_mode": { 360 | "name": "ipython", 361 | "version": 3 362 | }, 363 | "file_extension": ".py", 364 | "mimetype": "text/x-python", 365 | "name": "python", 366 | "nbconvert_exporter": "python", 367 | "pygments_lexer": "ipython3", 368 | "version": "3.5.0" 369 | } 370 | }, 371 | "nbformat": 4, 372 | "nbformat_minor": 0 373 | } 374 | -------------------------------------------------------------------------------- /Python Overview Part 3.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "cities = ['NY','LA','SF']" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": { 18 | "collapsed": true 19 | }, 20 | "outputs": [], 21 | "source": [ 22 | "city = cities[0]" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 3, 28 | "metadata": { 29 | "collapsed": false 30 | }, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "'NY'" 36 | ] 37 | }, 38 | "execution_count": 3, 39 | "metadata": {}, 40 | "output_type": "execute_result" 41 | } 42 | ], 43 | "source": [ 44 | "city" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 12, 50 | "metadata": { 51 | "collapsed": false 52 | }, 53 | "outputs": [ 54 | { 55 | "name": "stdout", 56 | "output_type": "stream", 57 | "text": [ 58 | "Party\n", 59 | "It's hot here\n", 60 | "Where am I?\n" 61 | ] 62 | } 63 | ], 64 | "source": [ 65 | "for city in cities:\n", 66 | " if city == 'NY':\n", 67 | " print(\"Party\")\n", 68 | " elif city == 'LA':\n", 69 | " print(\"It's hot here\")\n", 70 | " else:\n", 71 | " print('Where am I?')" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 8, 77 | "metadata": { 78 | "collapsed": true 79 | }, 80 | "outputs": [], 81 | "source": [ 82 | "city = cities[2]" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 13, 88 | "metadata": { 89 | "collapsed": true 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "t = (1,2,3)" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 14, 99 | "metadata": { 100 | "collapsed": false 101 | }, 102 | "outputs": [ 103 | { 104 | "data": { 105 | "text/plain": [ 106 | "(1, 2, 3)" 107 | ] 108 | }, 109 | "execution_count": 14, 110 | "metadata": {}, 111 | "output_type": "execute_result" 112 | } 113 | ], 114 | "source": [ 115 | "t" 116 | ] 117 | }, 118 | { 119 | "cell_type": "code", 120 | "execution_count": 15, 121 | "metadata": { 122 | "collapsed": false 123 | }, 124 | "outputs": [ 125 | { 126 | "ename": "AttributeError", 127 | "evalue": "'tuple' object has no attribute 'append'", 128 | "output_type": "error", 129 | "traceback": [ 130 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 131 | "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", 132 | "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", 133 | "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'append'" 134 | ] 135 | } 136 | ], 137 | "source": [ 138 | "t.append(2)" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 16, 144 | "metadata": { 145 | "collapsed": true 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "my_list = [1,2,3]" 150 | ] 151 | }, 152 | { 153 | "cell_type": "code", 154 | "execution_count": 17, 155 | "metadata": { 156 | "collapsed": true 157 | }, 158 | "outputs": [], 159 | "source": [ 160 | "my_dict = {'Joe':22,'Mike':12,}" 161 | ] 162 | }, 163 | { 164 | "cell_type": "code", 165 | "execution_count": 18, 166 | "metadata": { 167 | "collapsed": false 168 | }, 169 | "outputs": [ 170 | { 171 | "data": { 172 | "text/plain": [ 173 | "22" 174 | ] 175 | }, 176 | "execution_count": 18, 177 | "metadata": {}, 178 | "output_type": "execute_result" 179 | } 180 | ], 181 | "source": [ 182 | "my_dict['Joe']" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": 19, 188 | "metadata": { 189 | "collapsed": false 190 | }, 191 | "outputs": [ 192 | { 193 | "data": { 194 | "text/plain": [ 195 | "2" 196 | ] 197 | }, 198 | "execution_count": 19, 199 | "metadata": {}, 200 | "output_type": "execute_result" 201 | } 202 | ], 203 | "source": [ 204 | "len(my_dict)" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "execution_count": 21, 210 | "metadata": { 211 | "collapsed": true 212 | }, 213 | "outputs": [], 214 | "source": [ 215 | "def adder(x,y):\n", 216 | " \"\"\" This function will add x and y together \"\"\"\n", 217 | " answer = x + y\n", 218 | " return answer" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 22, 224 | "metadata": { 225 | "collapsed": false 226 | }, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "15" 232 | ] 233 | }, 234 | "execution_count": 22, 235 | "metadata": {}, 236 | "output_type": "execute_result" 237 | } 238 | ], 239 | "source": [ 240 | "adder(5,10)" 241 | ] 242 | }, 243 | { 244 | "cell_type": "code", 245 | "execution_count": null, 246 | "metadata": { 247 | "collapsed": true 248 | }, 249 | "outputs": [], 250 | "source": [] 251 | } 252 | ], 253 | "metadata": { 254 | "kernelspec": { 255 | "display_name": "Python 3", 256 | "language": "python", 257 | "name": "python3" 258 | }, 259 | "language_info": { 260 | "codemirror_mode": { 261 | "name": "ipython", 262 | "version": 3 263 | }, 264 | "file_extension": ".py", 265 | "mimetype": "text/x-python", 266 | "name": "python", 267 | "nbconvert_exporter": "python", 268 | "pygments_lexer": "ipython3", 269 | "version": "3.5.0" 270 | } 271 | }, 272 | "nbformat": 4, 273 | "nbformat_minor": 0 274 | } 275 | -------------------------------------------------------------------------------- /lec25.csv: -------------------------------------------------------------------------------- 1 | q,r,s,t,apple 2 | 2,3,4,5,pear 3 | a,s,d,f,rabbit 4 | 5,2,5,7,dog -------------------------------------------------------------------------------- /myarray.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/m6jones/Course-Notes---Learning-Python-for-Data-Analysis-and-Visualization-by-Jose-Portilla/39dcca6cd155c2d91404750016545a27b6a43b21/myarray.npy -------------------------------------------------------------------------------- /mytextarray.txt: -------------------------------------------------------------------------------- 1 | 1.000000000000000000e+00,2.000000000000000000e+00,3.000000000000000000e+00 2 | 4.000000000000000000e+00,5.000000000000000000e+00,6.000000000000000000e+00 3 | -------------------------------------------------------------------------------- /mytextdata_out.csv: -------------------------------------------------------------------------------- 1 | ,0,1,2,3,4 2 | 0,q,r,s,t,apple 3 | 1,2,3,4,5,pear 4 | 2,a,s,d,f,rabbit 5 | 3,5,2,5,7,dog 6 | -------------------------------------------------------------------------------- /ziparray.npz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/m6jones/Course-Notes---Learning-Python-for-Data-Analysis-and-Visualization-by-Jose-Portilla/39dcca6cd155c2d91404750016545a27b6a43b21/ziparray.npz --------------------------------------------------------------------------------