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@brianray
Created June 10, 2018 17:46
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext rpy2.ipython\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>petal length (cm)</th>\n",
" <th>petal width (cm)</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" length width petal length (cm) petal width (cm) target\n",
"0 5.1 3.5 1.4 0.2 0.0\n",
"1 4.9 3.0 1.4 0.2 0.0\n",
"2 4.7 3.2 1.3 0.2 0.0\n",
"3 4.6 3.1 1.5 0.2 0.0\n",
"4 5.0 3.6 1.4 0.2 0.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from sklearn import datasets\n",
"import numpy as np\n",
"\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"sns.set(style=\"white\", color_codes=True)\n",
" \n",
"iris = datasets.load_iris()\n",
"df = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
" columns= iris['feature_names'] + ['target'])\n",
"df.rename(columns={'sepal length (cm)': 'length', 'sepal width (cm)': 'width'}, inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10cbf2898>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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fikHIsUa5GMRY810pbXE0Dnv2ThCjrY5eczn2eLCHPfX3jrZFrr6llverWuK0xWYCqK6u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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cbf2208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind=\"scatter\", x=\"length\", y=\"width\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QAAmQAAmQQCgBBuBQIvxMAiRAAiRAAhYQYAC2ADKLIAESIAESIIFQAnwKOpQI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\nCIIn6wAAAABJRU5ErkJggg==\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -i df\n",
"# may need to install.packages ggplot2\n",
"library('ggplot2')\n",
"ggplot(df, aes(x=df$width, y=df$length)) + geom_point()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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