{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import scikit_na as na\n",
"data = pd.read_csv('../../_tests/data/titanic_dataset.csv')"
]
},
{
"source": [
"## Heatmap\n",
"\n",
"### NA values\n",
"\n",
"Missing data can be visualized on a heatmap to quickly grasp its patterns. We will be using\n",
"[Altair](https://altair-viz.github.io) + [Vega](https://vega.github.io/vega-lite/)\n",
"backend. To plot a heatmap of NAs, simply pass your DataFrame to `scikit_na.altair.plot_heatmap()` function.\n",
"\n",
"Droppables are those values that will be dropped if we simply use ``pandas.DataFrame.dropna()`` on the *whole dataset*. By default, columns are sorted by the number of NA values."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "\n\n",
"text/plain": [
"alt.Chart(...)"
]
},
"metadata": {},
"execution_count": null
}
],
"source": [
"na.altair.plot_heatmap(data)"
]
},
{
"source": [
"### Correlations"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"Correlations can be plotted using `scikit_na.altair.plot_corr()` function. Under the hood, it calls `scikit_na.correlate()` function with your input DataFrame as the first argument:"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "\n\n",
"text/plain": [
"alt.LayerChart(...)"
]
},
"metadata": {},
"execution_count": null
}
],
"source": [
"na.altair.plot_corr(data).properties(width=125, height=125)"
]
},
{
"source": [
"## Stairs plot\n",
"\n",
"Stairs plot is a useful visualization of a dataset shrinkage on applying\n",
"``pandas.Series.dropna()`` method to each column sequentially (sorted by the\n",
"number of NA values, by default):"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": null,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"na.altair.plot_stairs(data)"
]
},
{
"source": [
"After dropping all NAs in *Cabin* column, we are left with 21 more NAs (in *Age*\n",
"and *Embarked* columns). This plot also shows tooltips with exact numbers of NA\n",
"values that are dropped per each column."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "\n\n",
"text/plain": [
"alt.Chart(...)"
]
},
"metadata": {},
"execution_count": null
}
],
"source": [
"na.altair.plot_stairbars(data)"
]
},
{
"source": [
"## Histogram\n",
"\n",
"Plotting a nice histogram may require configuring additional parameters."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "\n\n",
"text/plain": [
"alt.Chart(...)"
]
},
"metadata": {},
"execution_count": null
}
],
"source": [
"chart = na.altair.plot_hist(data, col='Pclass', col_na='Age')\\\n",
" .properties(width=200, height=200)\n",
"chart.configure_axisX(labelAngle = 0)"
]
},
{
"source": [
"## Density plot"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "\n\n",
"text/plain": [
"alt.Chart(...)"
]
},
"metadata": {},
"execution_count": null
}
],
"source": [
"chart = na.altair.plot_kde(data, col='Age', col_na='Cabin')\\\n",
" .properties(width=200, height=200)\n",
"chart.configure_axisX(labelAngle = 0)"
]
}
],
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"kernelspec": {
"display_name": "Python 3.9.5 64-bit",
"language": "python",
"name": "python39564bit37ed7351e0344c1faf3897132336b259"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5-final"
}
},
"nbformat": 4,
"nbformat_minor": 4
}