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glampglamp
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inline matplotlibs
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‎notebooks/01 - The Basics - The API, Datasets, Your First ggplot.ipynb‎

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@@ -20,6 +20,17 @@
2020
"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 2
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"%matplotlib inline"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 1
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},
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{
@@ -58,7 +69,7 @@
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1944-01-01 00:00:00</td>\n",
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" <td>1944-01-01</td>\n",
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" <td> 751</td>\n",
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" <td> 85</td>\n",
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" <td> 1280</td>\n",
@@ -69,7 +80,7 @@
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1944-02-01 00:00:00</td>\n",
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" <td>1944-02-01</td>\n",
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" <td> 713</td>\n",
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" <td> 77</td>\n",
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" <td> 1169</td>\n",
@@ -80,7 +91,7 @@
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1944-03-01 00:00:00</td>\n",
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" <td>1944-03-01</td>\n",
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" <td> 741</td>\n",
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" <td> 90</td>\n",
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" <td> 1128</td>\n",
@@ -91,7 +102,7 @@
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1944-04-01 00:00:00</td>\n",
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" <td>1944-04-01</td>\n",
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" <td> 650</td>\n",
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" <td> 89</td>\n",
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" <td> 978</td>\n",
@@ -102,7 +113,7 @@
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1944-05-01 00:00:00</td>\n",
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" <td>1944-05-01</td>\n",
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" <td> 681</td>\n",
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" <td> 106</td>\n",
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" <td> 1029</td>\n",
@@ -113,29 +124,32 @@
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows \u00d7 8 columns</p>\n",
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"</div>"
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],
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 2,
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"prompt_number": 3,
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"text": [
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" date beef veal pork lamb_and_mutton broilers \\\n",
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"0 1944-01-01 00:00:00 751 85 1280 89 NaN \n",
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"1 1944-02-01 00:00:00 713 77 1169 72 NaN \n",
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"2 1944-03-01 00:00:00 741 90 1128 75 NaN \n",
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"3 1944-04-01 00:00:00 650 89 978 66 NaN \n",
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"4 1944-05-01 00:00:00 681 106 1029 78 NaN \n",
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" date beef veal pork lamb_and_mutton broilers other_chicken \\\n",
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"0 1944-01-01 751 85 1280 89 NaN NaN \n",
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"1 1944-02-01 713 77 1169 72 NaN NaN \n",
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"2 1944-03-01 741 90 1128 75 NaN NaN \n",
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"3 1944-04-01 650 89 978 66 NaN NaN \n",
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"4 1944-05-01 681 106 1029 78 NaN NaN \n",
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"\n",
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" turkey \n",
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"0 NaN \n",
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"1 NaN \n",
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"2 NaN \n",
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"3 NaN \n",
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"4 NaN \n",
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"\n",
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" other_chicken turkey \n",
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"0 NaN NaN \n",
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"1 NaN NaN \n",
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"2 NaN NaN \n",
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"3 NaN NaN \n",
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"4 NaN NaN "
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"[5 rows x 8 columns]"
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]
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}
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],
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"prompt_number": 2
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"prompt_number": 3
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},
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{
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"cell_type": "code",
@@ -233,22 +247,25 @@
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows \u00d7 10 columns</p>\n",
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"</div>"
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],
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 3,
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"prompt_number": 4,
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"text": [
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" carat cut color clarity depth table price x y z\n",
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"0 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43\n",
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"1 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31\n",
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"2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31\n",
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"3 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63\n",
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"4 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75"
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"4 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75\n",
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"\n",
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"[5 rows x 10 columns]"
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]
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}
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],
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"prompt_number": 3
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"prompt_number": 4
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},
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{
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"cell_type": "code",
@@ -358,22 +375,32 @@
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows \u00d7 12 columns</p>\n",
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"</div>"
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],
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 6,
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"prompt_number": 5,
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"text": [
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" name mpg cyl disp hp drat wt qsec vs am gear carb\n",
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"0 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n",
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"1 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n",
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"2 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n",
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"3 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n",
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"4 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2"
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" name mpg cyl disp hp drat wt qsec vs am gear \\\n",
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"0 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 \n",
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"1 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 \n",
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"2 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 \n",
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"3 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 \n",
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"4 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 \n",
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"\n",
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" carb \n",
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"0 4 \n",
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"1 4 \n",
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"2 1 \n",
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"3 1 \n",
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"4 2 \n",
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"\n",
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"[5 rows x 12 columns]"
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]
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}
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],
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"prompt_number": 6
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"prompt_number": 5
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},
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{
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"cell_type": "code",
@@ -477,7 +504,7 @@
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 13
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"prompt_number": 6
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},
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{
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"cell_type": "code",
@@ -488,7 +515,7 @@
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 14
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},
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{
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"cell_type": "markdown",
@@ -511,19 +538,19 @@
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"output_type": "display_data",
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Y1jtbUXeOsHWR2LoYirZzxI1tvOo9S6XSml/gzJkzcf/990epVIpms7nic+fOnYvx8fEV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\n",
513540
"text": [
514-
"<matplotlib.figure.Figure at 0x10616ffd0>"
541+
"<matplotlib.figure.Figure at 0x10c214350>"
515542
]
516543
},
517544
{
518545
"metadata": {},
519546
"output_type": "pyout",
520-
"prompt_number": 15,
547+
"prompt_number": 8,
521548
"text": [
522-
"<ggplot: (272343697)>"
549+
"<ggplot: (281151189)>"
523550
]
524551
}
525552
],
526-
"prompt_number": 15
553+
"prompt_number": 8
527554
},
528555
{
529556
"cell_type": "markdown",
@@ -547,13 +574,13 @@
547574
{
548575
"metadata": {},
549576
"output_type": "pyout",
550-
"prompt_number": 19,
577+
"prompt_number": 9,
551578
"text": [
552579
"{'y': 'price', 'x': 'date'}"
553580
]
554581
}
555582
],
556-
"prompt_number": 19
583+
"prompt_number": 9
557584
},
558585
{
559586
"cell_type": "code",
@@ -568,13 +595,13 @@
568595
{
569596
"metadata": {},
570597
"output_type": "pyout",
571-
"prompt_number": 26,
598+
"prompt_number": 10,
572599
"text": [
573600
"{u'y': 'price', u'x': 'date'}"
574601
]
575602
}
576603
],
577-
"prompt_number": 26
604+
"prompt_number": 10
578605
},
579606
{
580607
"cell_type": "code",
@@ -589,13 +616,13 @@
589616
{
590617
"metadata": {},
591618
"output_type": "pyout",
592-
"prompt_number": 27,
619+
"prompt_number": 11,
593620
"text": [
594621
"{u'y': 'price', u'x': 'date', u'color': 'name'}"
595622
]
596623
}
597624
],
598-
"prompt_number": 27
625+
"prompt_number": 11
599626
},
600627
{
601628
"cell_type": "code",
@@ -610,13 +637,13 @@
610637
{
611638
"metadata": {},
612639
"output_type": "pyout",
613-
"prompt_number": 28,
640+
"prompt_number": 12,
614641
"text": [
615642
"{'color': 'date * price', 'y': 'price', 'shape': 'factor(name)', 'x': 'date'}"
616643
]
617644
}
618645
],
619-
"prompt_number": 28
646+
"prompt_number": 12
620647
},
621648
{
622649
"cell_type": "markdown",

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