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1 | { | |
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2 | "cells": [ | |
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3 | { | |
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4 | "cell_type": "markdown", | |
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5 | "metadata": {}, | |
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6 | "source": [ | |
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7 | "# Test notebook for overflowing content" | |
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8 | ] | |
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9 | }, | |
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10 | { | |
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11 | "cell_type": "markdown", | |
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12 | "metadata": {}, | |
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13 | "source": [ | |
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14 | "markdown image:\n", | |
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15 | "\n", | |
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16 | "<img src=\"http://placehold.it/800x200.png\">\n", | |
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17 | "\n", | |
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18 | "unconfined markdown image:\n", | |
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19 | "\n", | |
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20 | "<img src=\"http://placehold.it/800x200.png\" class=\"unconfined\">" | |
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21 | ] | |
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22 | }, | |
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23 | { | |
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24 | "cell_type": "code", | |
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25 | "execution_count": 13, | |
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26 | "metadata": { | |
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27 | "collapsed": true | |
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28 | }, | |
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29 | "outputs": [], | |
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30 | "source": [ | |
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31 | "from IPython.display import Image, IFrame" | |
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32 | ] | |
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33 | }, | |
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34 | { | |
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35 | "cell_type": "markdown", | |
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36 | "metadata": {}, | |
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37 | "source": [ | |
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38 | "Overflow image in HTML (non-embedded)" | |
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39 | ] | |
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40 | }, | |
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41 | { | |
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42 | "cell_type": "code", | |
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43 | "execution_count": 22, | |
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44 | "metadata": { | |
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45 | "collapsed": false | |
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46 | }, | |
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47 | "outputs": [ | |
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48 | { | |
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49 | "data": { | |
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50 | "text/html": [ | |
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51 | "<img src=\"http://placehold.it/800x200.png\"/>" | |
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52 | ], | |
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53 | "text/plain": [ | |
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54 | "<IPython.core.display.Image object>" | |
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55 | ] | |
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56 | }, | |
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57 | "execution_count": 22, | |
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58 | "metadata": {}, | |
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59 | "output_type": "execute_result" | |
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60 | } | |
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61 | ], | |
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62 | "source": [ | |
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63 | "Image(url=\"http://placehold.it/800x200.png\", embed=False)" | |
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64 | ] | |
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65 | }, | |
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66 | { | |
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67 | "cell_type": "markdown", | |
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68 | "metadata": {}, | |
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69 | "source": [ | |
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70 | "Overflow image:" | |
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71 | ] | |
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72 | }, | |
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73 | { | |
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74 | "cell_type": "code", | |
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75 | "execution_count": 4, | |
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76 | "metadata": { | |
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77 | "collapsed": false | |
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78 | }, | |
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79 | "outputs": [ | |
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80 | { | |
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81 | "data": { | |
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82 | "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAADIBAMAAADioZgpAAAAG1BMVEXMzMyWlpbFxcWxsbGjo6Oc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\nLzkAAAAASUVORK5CYII=\n", | |
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83 | "text/plain": [ | |
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84 | "<IPython.core.display.Image object>" | |
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85 | ] | |
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86 | }, | |
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87 | "execution_count": 4, | |
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88 | "metadata": {}, | |
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89 | "output_type": "execute_result" | |
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90 | } | |
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91 | ], | |
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92 | "source": [ | |
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93 | "Image(url=\"http://placehold.it/800x200.png\", embed=True)" | |
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94 | ] | |
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95 | }, | |
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96 | { | |
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97 | "cell_type": "markdown", | |
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98 | "metadata": {}, | |
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99 | "source": [ | |
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100 | "Overflow with explicit height, width (retina):" | |
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101 | ] | |
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102 | }, | |
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103 | { | |
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104 | "cell_type": "code", | |
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105 | "execution_count": 23, | |
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106 | "metadata": { | |
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107 | "collapsed": false | |
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108 | }, | |
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109 | "outputs": [ | |
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110 | { | |
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111 | "data": { | |
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113 | "text/plain": [ | |
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114 | "<IPython.core.display.Image object>" | |
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115 | ] | |
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116 | }, | |
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117 | "execution_count": 23, | |
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118 | "metadata": { | |
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119 | "image/jpeg": { | |
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120 | "height": 100, | |
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121 | "width": 900 | |
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122 | } | |
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123 | }, | |
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124 | "output_type": "execute_result" | |
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125 | } | |
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126 | ], | |
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127 | "source": [ | |
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128 | "Image(url=\"http://placehold.it/1800x200.jpg\", embed=True, retina=True)" | |
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129 | ] | |
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130 | }, | |
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131 | { | |
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132 | "cell_type": "markdown", | |
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133 | "metadata": {}, | |
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134 | "source": [ | |
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135 | "Overflowing IFrame:" | |
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136 | ] | |
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137 | }, | |
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138 | { | |
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139 | "cell_type": "code", | |
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140 | "execution_count": 27, | |
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141 | "metadata": { | |
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142 | "collapsed": false | |
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143 | }, | |
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144 | "outputs": [ | |
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145 | { | |
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146 | "data": { | |
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147 | "text/html": [ | |
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148 | "\n", | |
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149 | " <iframe\n", | |
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150 | " width=\"900\"\n", | |
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151 | " height=\"400\"\n", | |
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152 | " src=\"http://ipython.org\"\n", | |
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153 | " frameborder=\"0\"\n", | |
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154 | " allowfullscreen\n", | |
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155 | " ></iframe>\n", | |
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156 | " " | |
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157 | ], | |
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158 | "text/plain": [ | |
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159 | "<IPython.lib.display.IFrame at 0x108cdbe48>" | |
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160 | ] | |
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161 | }, | |
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162 | "execution_count": 27, | |
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163 | "metadata": {}, | |
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164 | "output_type": "execute_result" | |
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165 | } | |
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166 | ], | |
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167 | "source": [ | |
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168 | "IFrame(src=\"http://ipython.org\", width=900, height=400)" | |
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169 | ] | |
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170 | }, | |
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171 | { | |
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172 | "cell_type": "markdown", | |
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173 | "metadata": {}, | |
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174 | "source": [ | |
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175 | "Overflowing table:" | |
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176 | ] | |
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177 | }, | |
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178 | { | |
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179 | "cell_type": "code", | |
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180 | "execution_count": 21, | |
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181 | "metadata": { | |
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182 | "collapsed": false | |
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183 | }, | |
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184 | "outputs": [ | |
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185 | { | |
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186 | "data": { | |
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187 | "text/html": [ | |
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188 | "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", | |
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189 | "<table border=\"1\" class=\"dataframe\">\n", | |
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190 | " <thead>\n", | |
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191 | " <tr style=\"text-align: right;\">\n", | |
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192 | " <th></th>\n", | |
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193 | " <th>0</th>\n", | |
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194 | " <th>1</th>\n", | |
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195 | " <th>2</th>\n", | |
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196 | " <th>3</th>\n", | |
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197 | " <th>4</th>\n", | |
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198 | " <th>5</th>\n", | |
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199 | " <th>6</th>\n", | |
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200 | " <th>7</th>\n", | |
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201 | " <th>8</th>\n", | |
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202 | " <th>9</th>\n", | |
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203 | " <th>10</th>\n", | |
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204 | " <th>11</th>\n", | |
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205 | " <th>12</th>\n", | |
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206 | " <th>13</th>\n", | |
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207 | " <th>14</th>\n", | |
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208 | " </tr>\n", | |
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209 | " </thead>\n", | |
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210 | " <tbody>\n", | |
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211 | " <tr>\n", | |
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212 | " <th>0</th>\n", | |
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213 | " <td> column</td>\n", | |
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214 | " <td> column</td>\n", | |
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215 | " <td> column</td>\n", | |
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216 | " <td> column</td>\n", | |
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217 | " <td> column</td>\n", | |
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218 | " <td> column</td>\n", | |
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219 | " <td> column</td>\n", | |
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220 | " <td> column</td>\n", | |
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221 | " <td> column</td>\n", | |
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222 | " <td> column</td>\n", | |
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223 | " <td> column</td>\n", | |
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224 | " <td> column</td>\n", | |
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225 | " <td> column</td>\n", | |
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226 | " <td> column</td>\n", | |
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227 | " <td> column</td>\n", | |
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228 | " </tr>\n", | |
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229 | " </tbody>\n", | |
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230 | "</table>\n", | |
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231 | "</div>" | |
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232 | ], | |
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233 | "text/plain": [ | |
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234 | " 0 1 2 3 4 5 6 7 8 \\\n", | |
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235 | "0 column column column column column column column column column \n", | |
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236 | "\n", | |
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237 | " 9 10 11 12 13 14 \n", | |
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238 | "0 column column column column column column " | |
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239 | ] | |
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240 | }, | |
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241 | "execution_count": 21, | |
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242 | "metadata": {}, | |
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243 | "output_type": "execute_result" | |
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244 | } | |
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245 | ], | |
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246 | "source": [ | |
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247 | "import pandas as pd\n", | |
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248 | "pd.DataFrame([['column'] * 15])" | |
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249 | ] | |
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250 | } | |
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251 | ], | |
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252 | "metadata": { | |
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253 | "kernelspec": { | |
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254 | "display_name": "Python 3", | |
|
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255 | "language": "python", | |
|
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256 | "name": "python3" | |
|
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257 | }, | |
|
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258 | "language_info": { | |
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259 | "codemirror_mode": { | |
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260 | "name": "ipython", | |
|
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261 | "version": 3 | |
|
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262 | }, | |
|
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263 | "file_extension": ".py", | |
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264 | "mimetype": "text/x-python", | |
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265 | "name": "python", | |
|
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266 | "nbconvert_exporter": "python", | |
|
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267 | "pygments_lexer": "ipython3", | |
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268 | "version": "3.4.3" | |
|
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269 | } | |
|
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270 | }, | |
|
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271 | "nbformat": 4, | |
|
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272 | "nbformat_minor": 0 | |
|
|
273 | } | |
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