tutorial_plotting.ipyml

cells:

- markdown: |
    # Plotting


- markdown: |
    Import the LArray library:


- code: |
    from larray import *


- markdown: |
    Import the test array `population` from the `demography_eurostat` dataset:


- code: |
    demography_eurostat = load_example_data('demography_eurostat')
    population = demography_eurostat.population / 1_000_000
    
    # show the 'population' array
    population


- markdown: |
    Inline matplotlib (required in notebooks):


- code: |
    %matplotlib inline


- markdown: |
    ## Python Plot (matplotlib)


- markdown: |
    In a Python script, add the following import on top of the script:


- code: |
    import matplotlib.pyplot as plt


- markdown: |
    Create and show a simple plot (last axis define the different curves to draw):


- code: |
    population['Belgium'].plot()
    # shows the figure
    plt.show()


- markdown: |
    - Create a Line plot with grid, title, label on y axis and user-defined xticks. 
    - Save the plot as a png file (using `plt.savefig()`).
    - Show the plot:


- code: |
    population['Belgium'].plot(grid=True, xticks=population.time, title='Belgium')
    # add a label aling the y axis
    plt.ylabel('population (millions)')
    # saves figure in a file (see matplotlib.pyplot.savefig documentation for more details)
    plt.savefig('Belgium_population.png')
    # WARNING: show() reset the current figure after showing it! Do not call it before savefig
    plt.show()


- markdown: |
    Specify line styles and width:


- code: |
    # line styles: '-' for solid line, '--' for dashed line, '-.' for dash-dotted line and ':' for dotted line
    population['Male'].plot(style=['-', '--', '-.'], linewidth=2, xticks=population.time, title='Male')
    plt.ylabel('population (millions)')
    plt.show()


- markdown: |
    Move the legend inside the graph (using `plt.legend(loc='position')`):


- code: |
    population['Belgium'].plot(xticks=population.time, title='Male')
    plt.ylabel('population (millions)')
    # available values for loc are: 
    # 'best' (default), 'upper right', 'upper left', 'lower left', 'lower right', 'right',
    # center left', 'center right', 'lower center', 'upper center', 'center'
    plt.legend(loc='lower right')
    plt.show()


- markdown: |
    Put the legend outside the graph (using `plt.legend(bbox_to_anchor=(x, y))`):


- code: |
    population['Belgium'].plot(xticks=population.time, title='Male')
    plt.ylabel('population (millions)')
    plt.legend(bbox_to_anchor=(1.25, 0.6))
    plt.show()


- markdown: |
    Create a Bar plot:


- code: |
    population['Belgium'].plot.bar(title='Belgium')
    plt.ylabel('population (millions)')
    plt.legend(bbox_to_anchor=(1.25, 0.6))
    plt.show()


- markdown: |
    Create a _stacked_ Bar plot:


- code: |
    population['Belgium'].plot.bar(title='Belgium', stacked=True)
    plt.ylabel('population (millions)')
    plt.legend(bbox_to_anchor=(1.25, 0.6))
    plt.show()


- markdown: |
    Create a multiplot figure (using `plt.subplot(nrows,ncols,index)`):


- code: |
    figure, axes = plt.subplots(nrows=len(population.country), ncols=1, sharex=True, figsize=(5, 15))
    
    for row, c in enumerate(population.country):  
        population[c].plot(ax=axes[row], title=str(c))
        plt.ylabel('population (millions)')
        
    plt.xticks(population.time)
    plt.show()


- markdown: |
    See [plot](../_generated/larray.Array.plot.rst#larray.Array.plot) for more details and examples.
    
    See [pyplot tutorial](https://matplotlib.org/tutorials/introductory/pyplot.html) for a short introduction to `matplotlib.pyplot`.


- markdown: |
    ## Excel Plot


- markdown: |
    It is possible to dump arrays and to make plots in the same time in an Excel workbook:


- markdown: |
    ```python
    # to create a new Excel file, argument overwrite_file must be set to True
    with open_excel('workbook_with_plots.xlsx', overwrite_file=True) as wb:
        # ---- dump data
        # add a new sheet 'BFG' and dump the data for Belgium in it 
        wb['BFG'] = population['Belgium'].dump()
        # store the BFG sheet in a local variable
        sh = wb['BFG']
        # dump the data for France using the equivalent method add_table()
        sh['A18'].add_table(population['France'])
        # dump the data for Germany
        sh['A35'].add_table(population['Germany'])
        
        # ---- use data to create plots
        # basic plot anchored to cell H1 using data for Belgium
        sh['H1'].add_plot('A1', title='Belgium population')
        # basic plot anchored to cell H18 using data for the France for years 2013 to 2016
        sh['H18'].add_plot('A18:E20', title='France population')
        # plot with options anchored to cell H35 using data for Germany
        sh["H35"].add_plot("A35", title="Germany population", width=500, height=300, 
                           xticks_spacing=2, min_y=40_000_000, max_y=41_500_000)
        
        # actually write data and plots in the Workbook
        wb.save()
        
    # the Workbook is automatically closed when getting out the block defined by the with statement
    ```


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