.. This document is auto-generated by the altair-gallery extension. Do not modify directly.

.. _gallery_poly_fit:


Polynomial Fit Plot
===================
This example shows how to overlay data with a fitted polynomial


.. altair-plot::
    :code-below:

    
    import numpy as np
    import pandas as pd
    import altair as alt

    # Generate some random data
    rng = np.random.RandomState(1)
    x = rng.rand(40) ** 2
    y = 10 - 1. / (x + 0.1) + rng.randn(40)
    df = pd.DataFrame({'x': x, 'y': y})

    # Define the degree of the polynomial fit
    degree_list = [1, 3, 5]

    # Build a dataframe with the fitted data
    poly_data = pd.DataFrame({'xfit': np.linspace(df['x'].min(), df['x'].max(), 500)})

    for degree in degree_list:
        poly_data[str(degree)] = np.poly1d(np.polyfit(df['x'], df['y'], degree))(poly_data['xfit'])

    # Tidy the dataframe so 'degree' is a variable
    poly_data = pd.melt(poly_data,
                        id_vars=['xfit'],
                        value_vars=[str(deg) for deg in degree_list],
                        var_name='degree', value_name='yfit')

    # Plot the data points on an interactive axis
    points = alt.Chart(df).mark_circle(color='black').encode(
        x=alt.X('x', axis=alt.Axis(title='x')),
        y=alt.Y('y', axis=alt.Axis(title='y')),
    ).interactive()

    # Plot the best fit polynomials
    polynomial_fit = alt.Chart(poly_data).mark_line().encode(
        x='xfit',
        y='yfit',
        color='degree'
    )

    points + polynomial_fit


.. toctree::
   :hidden: