alnoda-workspaces/workspaces/notebook-workspace/tutorials/lux.ipynb
2022-06-20 18:24:28 +00:00

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{
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{
"source": [
"# Intelligent Visual Discovery with Lux \n",
"Lux helps you to explore patterns and correlations in your dataframe\n",
"__Lux works with both Jupyter notebook and Jupyter Lab__\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import lux and pandas \n",
"import lux # enables Lux\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "18e8d3df",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('housing.csv') "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "17587d1b",
"metadata": {},
"outputs": [
{
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" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>b</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
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" <th>0</th>\n",
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"<p>506 rows × 14 columns</p>\n",
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"text/plain": [
" crim zn indus chas nox rm age dis rad tax \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 \n",
"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 \n",
"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 \n",
".. ... ... ... ... ... ... ... ... ... ... \n",
"501 0.06263 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 \n",
"502 0.04527 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 \n",
"503 0.06076 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 \n",
"504 0.10959 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 \n",
"505 0.04741 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 \n",
"\n",
" ptratio b lstat medv \n",
"0 15.3 396.90 4.98 24.0 \n",
"1 17.8 396.90 9.14 21.6 \n",
"2 17.8 392.83 4.03 34.7 \n",
"3 18.7 394.63 2.94 33.4 \n",
"4 18.7 396.90 5.33 36.2 \n",
".. ... ... ... ... \n",
"501 21.0 391.99 9.67 22.4 \n",
"502 21.0 396.90 9.08 20.6 \n",
"503 21.0 396.90 5.64 23.9 \n",
"504 21.0 393.45 6.48 22.0 \n",
"505 21.0 396.90 7.88 11.9 \n",
"\n",
"[506 rows x 14 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display pandas dataframe, click button 'Toggle Pandas/Lux'\n",
"df"
]
}
],
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