.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_intro_scipy_auto_examples_plot_normal_distribution.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_intro_scipy_auto_examples_plot_normal_distribution.py:


=======================================
Normal distribution: histogram and PDF
=======================================

Explore the normal distribution: a histogram built from samples and the
PDF (probability density function).




.. image:: /intro/scipy/auto_examples/images/sphx_glr_plot_normal_distribution_001.png
    :class: sphx-glr-single-img





.. code-block:: python


    import numpy as np

    # Sample from a normal distribution using numpy's random number generator
    samples = np.random.normal(size=10000)

    # Compute a histogram of the sample
    bins = np.linspace(-5, 5, 30)
    histogram, bins = np.histogram(samples, bins=bins, density=True)

    bin_centers = 0.5*(bins[1:] + bins[:-1])

    # Compute the PDF on the bin centers from scipy distribution object
    from scipy import stats
    pdf = stats.norm.pdf(bin_centers)

    from matplotlib import pyplot as plt
    plt.figure(figsize=(6, 4))
    plt.plot(bin_centers, histogram, label="Histogram of samples")
    plt.plot(bin_centers, pdf, label="PDF")
    plt.legend()
    plt.show()


**Total running time of the script:** ( 0 minutes  0.014 seconds)


.. _sphx_glr_download_intro_scipy_auto_examples_plot_normal_distribution.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_normal_distribution.py <plot_normal_distribution.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_normal_distribution.ipynb <plot_normal_distribution.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_