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240 lines
5.2 KiB
240 lines
5.2 KiB
5 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Seaborn demo per Jake VanderPlas below"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from __future__ import print_function, division\n",
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"\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.style.use('ggplot')\n",
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"x = np.linspace(0, 10, 1000)\n",
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"plt.plot(x, np.sin(x), x, np.cos(x));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"sns.set()\n",
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"plt.plot(x, np.sin(x), x, np.cos(x));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
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"data = pd.DataFrame(data, columns=['x', 'y'])\n",
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"\n",
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"for col in 'xy':\n",
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" plt.hist(data[col], density=True, alpha=0.5)\n",
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" # old Matplotlib would be plt.hist(data[col], normed=True, alpha=0.5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for col in 'xy':\n",
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" sns.kdeplot(data[col], shade=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.distplot(data['x']);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.kdeplot(data.x, data.y); # formerly sns.kdeplot(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with sns.axes_style('white'):\n",
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" sns.jointplot(\"x\", \"y\", data, kind='kde');"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with sns.axes_style('white'):\n",
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" sns.jointplot(\"x\", \"y\", data, kind='hex')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"iris = sns.load_dataset(\"iris\")\n",
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"iris.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tips = sns.load_dataset('tips')\n",
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"tips.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n",
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"\n",
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"grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
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"grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with sns.axes_style(style='ticks'):\n",
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" g = sns.catplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
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" g.set_axis_labels(\"Day\", \"Total Bill\");"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with sns.axes_style('white'):\n",
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" sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"planets = sns.load_dataset('planets')\n",
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"planets.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with sns.axes_style('white'):\n",
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" g = sns.catplot(\"year\", data=planets, aspect=1.5)\n",
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" g.set_xticklabels(step=5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"with sns.axes_style('white'):\n",
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" g = sns.catplot(\"year\", data=planets, aspect=4.0,\n",
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" hue='method', order=range(2001, 2015), kind=\"count\")\n",
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" g.set_ylabels('Number of Planets Discovered')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Scikit-learn tutorial from pycon 2015 Jake VanderPlas [here](http://nbviewer.ipython.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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