import matplotlib.pyplot as plt
    import numpy as np
    from votes import wide as df

    # Initialise a figure. subplots() with no args gives one plot.
    fig, ax = plt.subplots()

    # A little data preparation
    years = df['year']
    x = np.arange(len(years))

    # Plot each bar plot. Note: manually calculating the 'dodges' of the bars
    ax.bar(x - 3*width/2, df['conservative'], width, label='Conservative', color='#0343df')
    ax.bar(x - width/2, df['labour'], width, label='Labour', color='#e50000')
    ax.bar(x + width/2, df['liberal'], width, label='Liberal', color='#ffff14')
    ax.bar(x + 3*width/2, df['others'], width, label='Others', color='#929591')

    # Customise some display properties
    ax.set_ylabel('Seats')
    ax.set_title('UK election results')
    ax.set_xticks(x)    # This ensures we have one tick per year, otherwise we get fewer
    ax.set_xticklabels(years.astype(str).values, rotation='vertical')
    ax.legend()

    # Ask Matplotlib to show the plot
    plt.show()

    import seaborn as sns
    from votes import long as df

    # Some boilerplate to initialise things
    sns.set()
    plt.figure()

    # This is where the actual plot gets made
    ax = sns.barplot(data=df, x="year", y="seats", hue="party", palette=['blue', 'red', 'yellow', 'grey'], saturation=0.6)

    # Customise some display properties
    ax.set_title('UK election results')
    ax.grid(color='#cccccc')
    ax.set_ylabel('Seats')
    ax.set_xlabel(None)
    ax.set_xticklabels(df["year"].unique().astype(str), rotation='vertical')

    # Ask Matplotlib to show it
    plt.show()

import plotly.graph_objects as go
    from votes import wide as df

    #  Get a convenient list of x-values
    years = df['year']
    x = list(range(len(years)))

    # Specify the plots
    bar_plots = [
        go.Bar(x=x, y=df['conservative'], name='Conservative', marker=go.bar.Marker(color='#0343df')),
        go.Bar(x=x, y=df['labour'], name='Labour', marker=go.bar.Marker(color='#e50000')),
        go.Bar(x=x, y=df['liberal'], name='Liberal', marker=go.bar.Marker(color='#ffff14')),
        go.Bar(x=x, y=df['others'], name='Others', marker=go.bar.Marker(color='#929591')),
    ]

    # Customise some display properties
    layout = go.Layout(
        title=go.layout.Title(text="Election results", x=0.5),
        yaxis_title="Seats",
        xaxis_tickmode="array",
        xaxis_tickvals=list(range(27)),
        xaxis_ticktext=tuple(df['year'].values),
    )

    # Make the multi-bar plot
    fig = go.Figure(data=bar_plots, layout=layout)

    # Tell Plotly to render it
    fig.show()

from bokeh.io import show, output_file
    from bokeh.models import ColumnDataSource, FactorRange, HoverTool
    from bokeh.plotting import figure
    from bokeh.transform import factor_cmap
    from votes import long as df

    # Specify a file to write the plot to
    output_file("elections.html")

    # Tuples of groups (year, party)
    x = [(str(r[1]['year']), r[1]['party']) for r in df.iterrows()]
    y = df['seats']

    # Bokeh wraps your data in its own objects to support interactivity
    source = ColumnDataSource(data=dict(x=x, y=y))

    # Create a colourmap
    cmap = {
        'Conservative': '#0343df',
        'Labour': '#e50000',
        'Liberal': '#ffff14',
        'Others': '#929591',
    }
    fill_color = factor_cmap('x', palette=list(cmap.values()), factors=list(cmap.keys()), start=1, end=2)

    # Make the plot
    p = figure(x_range=FactorRange(*x), width=1200, title="Election results")
    p.vbar(x='x', top='y', width=0.9, source=source, fill_color=fill_color, line_color=fill_color)

    # Customise some display properties
    p.y_range.start = 0
    p.x_range.range_padding = 0.1
    p.yaxis.axis_label = 'Seats'
    p.xaxis.major_label_orientation = 1
    p.xgrid.grid_line_color = None

import altair as alt
    from votes import long as df

    # Set up the colourmap
    cmap = {
        'Conservative': '#0343df',
        'Labour': '#e50000',
        'Liberal': '#ffff14',
        'Others': '#929591',
    }

    # Cast years to strings
    df['year'] = df['year'].astype(str)

    # Here's where we make the plot
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('party', title=None),
        y='seats',
        column=alt.Column('year', sort=list(df['year']), title=None),
        color=alt.Color('party', scale=alt.Scale(domain=list(cmap.keys()), range=list(cmap.values())))
    )

    # Save it as an HTML file.
    chart.save('altair-elections.html')

 import pygal
    from pygal.style import Style
    from votes import wide as df

    # Define the style
    custom_style = Style(
        colors=('#0343df', '#e50000', '#ffff14', '#929591')
        font_family='Roboto,Helvetica,Arial,sans-serif',
        background='transparent',
        label_font_size=14,
    )

    # Set up the bar plot, ready for data
    c = pygal.Bar(
        title="UK Election Results",
        style=custom_style,
        y_title='Seats',
        width=1200,
        x_label_rotation=270,
    )

    # Add four data sets to the bar plot
    c.add('Conservative', df['conservative'])
    c.add('Labour', df['labour'])
    c.add('Liberal', df['liberal'])
    c.add('Others', df['others'])

    # Define the X-labels
    c.x_labels = df['year']

    # Write this to an SVG file
    c.render_to_file('pygal.svg')

    from matplotlib.colors import ListedColormap
    from votes import wide as df

    cmap = ListedColormap(['#0343df', '#e50000', '#ffff14', '#929591'])

    ax = df.plot.bar(x='year', colormap=cmap)

    ax.set_xlabel(None)
    ax.set_ylabel('Seats')
    ax.set_title('UK election results')

    plt.show()