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@dataprofessor
dataprofessor authored Apr 22, 2020
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"source": [
"# **Minimal working example of pandas-profiling**\n",
"\n",
"Chanin Nantasenamat\n",
"\n",
"<i>[Data Professor YouTube channel](http://youtube.com/dataprofessor), http://youtube.com/dataprofessor </i>\n",
"\n",
"In this Jupyter notebook, a minimum working example (MWE) of pandas-profiling library is shown. The code is taken directly from the example given on the GitHub of pandas-profiling.\n",
"\n",
"Source: https://github.com/pandas-profiling/pandas-profiling\n",
"\n",
"See it in action below!\n",
"\n",
"## **If you find this useful, Please give this notebook a thumbs up on [Kaggle](https://www.kaggle.com/chaninnantasenamat/pandas-profiling-example)!** 👍👍\n",
"https://www.kaggle.com/chaninnantasenamat/pandas-profiling-example\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qkKPkN6ZQ1-9",
"colab_type": "text"
},
"source": [
"## **Import libraries**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pOoIHSWUQ1--",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas_profiling import ProfileReport"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Sz4zHJqQQ1_B",
"colab_type": "text"
},
"source": [
"## **Create synthetic data**"
]
},
{
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"metadata": {
"id": "JaTx3DQbQ1_C",
"colab_type": "code",
"colab": {}
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"source": [
"df = pd.DataFrame(\n",
" np.random.rand(100, 5),\n",
" columns=['a', 'b', 'c', 'd', 'e']\n",
")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wae5HPZEQ1_F",
"colab_type": "text"
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"source": [
"## **Create the Report**"
]
},
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"id": "OinxFMrdQ1_G",
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"profile = ProfileReport(df, title='Pandas Profiling Report', html={'style':{'full_width':True}})"
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"Report generated with <a href=\"https://github.com/pandas-profiling/pandas-profiling\">pandas-profiling</a>."
],
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"<IPython.core.display.HTML object>"
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