Data visualization: matplotlib & seaborn
========================================

Basic plots
-----------

.. code:: ipython3

    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    # inline plot (for jupyter)
    %matplotlib inline
    
    plt.figure(figsize=(9, 3))
    x = np.linspace(0, 10, 50)
    sinus = np.sin(x)
    
    plt.plot(x, sinus)
    plt.show()



.. image:: scipy_matplotlib_files/scipy_matplotlib_1_0.png


.. code:: ipython3

    plt.figure(figsize=(9, 3))
    
    plt.plot(x, sinus, "o")
    plt.show()
    # use plt.plot to get color / marker abbreviations



.. image:: scipy_matplotlib_files/scipy_matplotlib_2_0.png


.. code:: ipython3

    # Rapid multiplot
    
    plt.figure(figsize=(9, 3))
    cosinus = np.cos(x)
    plt.plot(x, sinus, "-b", x, sinus, "ob", x, cosinus, "-r", x, cosinus, "or")
    plt.xlabel('this is x!')
    plt.ylabel('this is y!')
    plt.title('My First Plot')
    plt.show()



.. image:: scipy_matplotlib_files/scipy_matplotlib_3_0.png


.. code:: ipython3

    # Step by step
    
    plt.figure(figsize=(9, 3))
    plt.plot(x, sinus, label='sinus', color='blue', linestyle='--', linewidth=2)
    plt.plot(x, cosinus, label='cosinus', color='red', linestyle='-', linewidth=2)
    plt.legend()
    plt.show()



.. image:: scipy_matplotlib_files/scipy_matplotlib_4_0.png


Scatter (2D) plots
------------------

Load dataset

.. code:: ipython3

    import pandas as pd
    try:
        salary = pd.read_csv("../datasets/salary_table.csv")
    except:
        url = 'https://github.com/duchesnay/pystatsml/raw/master/datasets/salary_table.csv'
        salary = pd.read_csv(url)
    
    df = salary
    print(df.head())


.. parsed-literal::

       salary  experience education management
    0   13876           1  Bachelor          Y
    1   11608           1      Ph.D          N
    2   18701           1      Ph.D          Y
    3   11283           1    Master          N
    4   11767           1      Ph.D          N


Simple scatter with colors
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    plt.figure(figsize=(3, 3), dpi=100)
    _ = sns.scatterplot(x="experience", y="salary", hue="education", data=salary)



.. image:: scipy_matplotlib_files/scipy_matplotlib_8_0.png


Legend outside

.. code:: ipython3

    ax = sns.relplot(x="experience", y="salary", hue="education", data=salary)



.. image:: scipy_matplotlib_files/scipy_matplotlib_10_0.png


Linear model
~~~~~~~~~~~~

.. code:: ipython3

    ax = sns.lmplot(x="experience", y="salary", hue="education", data=salary)



.. image:: scipy_matplotlib_files/scipy_matplotlib_12_0.png


Scatter plot with colors and symbols
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    ax = sns.relplot(x="experience", y="salary", hue="education", style='management', data=salary)



.. image:: scipy_matplotlib_files/scipy_matplotlib_14_0.png


Saving Figures
--------------

.. code:: ipython3

    ### bitmap format
    plt.plot(x, sinus)
    plt.savefig("sinus.png")
    plt.close()
    
    # Prefer vectorial format (SVG: Scalable Vector Graphics) can be edited with 
    # Inkscape, Adobe Illustrator, Blender, etc.
    plt.plot(x, sinus)
    plt.savefig("sinus.svg")
    plt.close()
    
    # Or pdf
    plt.plot(x, sinus)
    plt.savefig("sinus.pdf")
    plt.close()

Boxplot and violin plot: one factor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Box plots are non-parametric: they display variation in samples of a
statistical population without making any assumptions of the underlying
statistical distribution.

.. code:: ipython3

    ax = sns.boxplot(x="management", y="salary", data=salary)
    ax = sns.stripplot(x="management", y="salary", data=salary, jitter=True, color="black")



.. image:: scipy_matplotlib_files/scipy_matplotlib_18_0.png


.. code:: ipython3

    ax = sns.violinplot(x="management", y="salary", data=salary)
    ax = sns.stripplot(x="management", y="salary", data=salary, jitter=True, color="white")



.. image:: scipy_matplotlib_files/scipy_matplotlib_19_0.png


Boxplot and violin plot: two factors
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    ax = sns.boxplot(x="management", y="salary", hue="education", data=salary)
    ax = sns.stripplot(x="management", y="salary", hue="education", data=salary, jitter=True, dodge=True, linewidth=1)



.. image:: scipy_matplotlib_files/scipy_matplotlib_21_0.png


.. code:: ipython3

    ax = sns.violinplot(x="management", y="salary", hue="education", data=salary)
    ax = sns.stripplot(x="management", y="salary", hue="education", data=salary, jitter=True, dodge=True, linewidth=1)



.. image:: scipy_matplotlib_files/scipy_matplotlib_22_0.png


Distributions and density plot
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`Distributions with
seaborn <https://seaborn.pydata.org/tutorial/distributions.html>`__

.. code:: ipython3

    ax = sns.displot(x="salary", hue="management", kind="kde", data=salary, fill=True)



.. image:: scipy_matplotlib_files/scipy_matplotlib_24_0.png


Multiple axis
-------------

.. code:: ipython3

    fig, axes = plt.subplots(3, 1, figsize=(9, 9), sharex=True)
    
    i = 0
    for edu, d in salary.groupby(['education']):
        sns.kdeplot(x="salary", hue="management", data=d, fill=True, ax=axes[i], palette="muted")
        axes[i].set_title(edu)
        i += 1



.. image:: scipy_matplotlib_files/scipy_matplotlib_26_0.png


Pairwise scatter plots
----------------------

.. code:: ipython3

    ax = sns.pairplot(salary, hue="management")



.. image:: scipy_matplotlib_files/scipy_matplotlib_28_0.png


Time series
-----------

.. code:: ipython3

    import seaborn as sns
    sns.set(style="darkgrid")
    
    # Load an example dataset with long-form data
    fmri = sns.load_dataset("fmri")
    
    # Plot the responses for different events and regions
    ax = sns.pointplot(x="timepoint", y="signal",
                 hue="region", style="event",
                 data=fmri)



.. image:: scipy_matplotlib_files/scipy_matplotlib_30_0.png