import chartify

    # Blank charts tell you how to fill in the labels
    ch = chartify.Chart()
    ch.show('png')

WARNING:bokeh.core.validation.check:W-1000 (MISSING_RENDERERS): Plot has no renderers: figure(id='p1001', ...)

Scatter plot.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            x_column (str): Column name to plot on the x axis.
            y_column (str): Column name to plot on the y axis.
            size_column (str, optional): Column name of numerical values
                to plot on the size dimension.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific sorting of the colors.
            alpha (float): Alpha value.
            marker (str): marker type. Valid types:
                'asterisk', 'circle', 'circle_cross', 'circle_x', 'cross',
                'diamond', 'diamond_cross', 'hex', 'inverted_triangle',
                'square', 'square_x', 'square_cross', 'triangle',
                'x', '*', '+', 'o', 'ox', 'o+'
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()
    

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.plot.scatter(data_frame=data, x_column="date", y_column="unit_price")
    ch.set_title("Scatterplot")
    ch.set_subtitle("Plot two numeric values.")
    ch.show('png')

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.plot.scatter(data_frame=data, x_column="date", y_column="unit_price", size_column="quantity")
    ch.set_title("Scatterplot")
    ch.set_subtitle("Optional 'size_column' argument for changing scatter size.")
    ch.show('png')

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.plot.scatter(
        data_frame=data,
        x_column="date",
        y_column="unit_price",
        size_column="quantity",
        color_column="fruit",
    )
    ch.set_title("Scatterplot")
    ch.set_subtitle("Optional 'color_column' argument for grouping by color.")
    ch.show('png')

Text plot.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            x_column (str): Column name to plot on the x axis.
            y_column (str): Column name to plot on the y axis.
            text_column (str): Column name to plot as text labels.
            color_column (str, optional): Column name to group by on the
                color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific sorting of the colors.
            font_size (str, optional): Size of text.
            x_offset (int, optional): # of pixels for horizontal text offset.
                Can be negative. Default: 0.
            y_offset (int, optional): # of pixels for vertical text offset.
                Can be negative. Default: 0.
            angle (int): Degrees from horizontal for text rotation.
            text_color (str): Color name or hex value.
                See chartify.color_palettes.show() for available color names.
                If omitted, will default to the next color in the
                current color palette.
        

    import chartify

    data = chartify.examples.example_data()

    # Manipulate the data
    price_and_quantity_by_country = data.groupby("country")[["total_price", "quantity"]].sum().reset_index()
    print(price_and_quantity_by_country.head())
    
  country  total_price  quantity
0      BR   208.553175       215
1      CA   473.922173       659
2      GB   370.257657       446
3      JP   263.204503       424
4      US   645.058909      1134

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.plot.scatter(
        data_frame=price_and_quantity_by_country,
        x_column="total_price",
        y_column="quantity",
        color_column="country",
    )
    ch.style.color_palette.reset_palette_order()
    ch.plot.text(
        data_frame=price_and_quantity_by_country,
        x_column="total_price",
        y_column="quantity",
        text_column="country",
        color_column="country",
        x_offset=1,
        y_offset=-1,
        font_size="10pt",
    )
    ch.set_title("Text")
    ch.set_subtitle("Labels for specific observations.")
    ch.show('png')

Line Chart.

        Note:
            This method will not automatically sort the x-axis.
            Try sorting the axis if the line graph looks strange.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            x_column (str): Column name to plot on the x axis.
            y_column (str): Column name to plot on the y axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific sorting of the colors.
            line_dash (str, optional): Dash style for the line. One of:
                - 'solid'
                - 'dashed'
                - 'dotted'
                - 'dotdash'
                - 'dashdot'
            line_width (int, optional): Width of the line
            alpha (float): Alpha value.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Sum price grouped by date
    price_by_date = data.groupby("date")["total_price"].sum().reset_index()  # Move 'date' from index to column
    print(price_by_date.head())
    
        date  total_price
0 2017-01-10     1.808778
1 2017-01-12     0.829621
2 2017-01-22     1.998476
3 2017-01-27     1.390764
4 2017-01-28     2.658465

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.set_title("Line charts")
    ch.set_subtitle("Plot two numeric values connected by an ordered line.")
    ch.plot.line(
        # Data must be sorted by x column
        data_frame=price_by_date.sort_values("date"),
        x_column="date",
        y_column="total_price",
    )
    ch.show('png')

    price_by_date_and_country = (
        data.groupby(["date", "fruit"])["total_price"]
        .sum()
        .reset_index()  # Move 'date' and 'country' from index to column
    )
    print(price_by_date_and_country.head())
    
        date   fruit  total_price
0 2017-01-10   Apple     1.808778
1 2017-01-12  Orange     0.829621
2 2017-01-22   Grape     1.998476
3 2017-01-27  Banana     1.390764
4 2017-01-28   Apple     2.658465

    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.set_title("Line charts - Grouped by color")
    ch.plot.line(
        # Data must be sorted by x column
        data_frame=price_by_date_and_country.sort_values("date"),
        x_column="date",
        y_column="total_price",
        color_column="fruit",
    )
    ch.show('png')

Area plot.

        Note:
            - When a single y_column is passed: Shade area between the
                y_values and zero.
            - Use `stacked` argument for stacked areas.
            - When both y_column and second_y_column are passed:
                Shade area between the two y_columns.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            x_column (str): Column name to plot on the x axis.
            y_column (str): Column name to plot on the y axis.
            second_y_column (str, optional): Column name to plot on
                the y axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific sorting of the colors.
            stacked (bool, optional): Stacked the areas.
                Only applicable with a single y_column.
                Default: False.
        

    import pandas as pd
    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    total_quantity_by_month_and_fruit = (
        data.groupby([data["date"] + pd.offsets.MonthBegin(-1), "fruit"])["quantity"]
        .sum()
        .reset_index()
        .rename(columns={"date": "month"})
        .sort_values("month")
    )
    print(total_quantity_by_month_and_fruit.head())
    
       month   fruit  quantity
0 2017-01-01   Apple         7
1 2017-01-01  Banana         6
2 2017-01-01   Grape         1
3 2017-01-01  Orange         2
4 2017-02-01   Apple         8

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.set_title("Stacked area")
    ch.set_subtitle("Represent changes in distribution.")
    ch.plot.area(
        data_frame=total_quantity_by_month_and_fruit,
        x_column="month",
        y_column="quantity",
        color_column="fruit",
        stacked=True,
    )
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.set_title("Unstacked area")
    ch.set_subtitle("Show overlapping values. Automatically adjusts opacity.")
    ch.plot.area(
        data_frame=total_quantity_by_month_and_fruit,
        x_column="month",
        y_column="quantity",
        color_column="fruit",
        stacked=False,
    )
    ch.show('png')

    # Sum price grouped by date
    price_by_date = (
        data.groupby(["date"])["total_price"]
        .agg(["mean", "std", "count"])
        .loc["2017-12-01":]
        .assign(
            lower_ci=lambda x: x["mean"] - 1.96 * x["std"] / x["count"] ** 0.5,
            upper_ci=lambda x: x["mean"] + 1.96 * x["std"] / x["count"] ** 0.5,
        )
        .reset_index()
    )
    print(price_by_date.head())
    
        date      mean       std  count  lower_ci  upper_ci
0 2017-12-01  2.130242  1.723854      3  0.179518  4.080967
1 2017-12-02  1.801198  1.385051     10  0.942735  2.659662
2 2017-12-03  2.157626  1.163018      7  1.296050  3.019202
3 2017-12-04  0.923048  0.472394      4  0.460102  1.385994
4 2017-12-05  2.179000  1.258695      7  1.246546  3.111454

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.set_title("Area with second_y_column")
    ch.set_subtitle("Use alone or combined with line graphs to represent confidence.")
    ch.plot.area(
        data_frame=price_by_date,
        x_column="date",
        y_column="lower_ci",
        second_y_column="upper_ci",
    )
    # Reset to ensure same color of line & shaded interval
    ch.style.color_palette.reset_palette_order()
    ch.plot.line(data_frame=price_by_date, x_column="date", y_column="mean")
    ch.show('png')

Bar chart.

        Note:
            To change the orientation set x_axis_type or y_axis_type
            argument of the Chart object.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific color sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'values'.
                - 'values': Order categorical axis by the numerical
                    axis values. Default.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional): Sort order of the
                categorical axis. Default False.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    quantity_by_fruit = data.groupby("fruit")["quantity"].sum().reset_index()

    print(quantity_by_fruit.head())
    
    fruit  quantity
0   Apple       608
1  Banana      1076
2   Grape       332
3  Orange       862

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Vertical bar plot")
    ch.set_subtitle("Automatically sorts by value counts.")
    ch.plot.bar(
        data_frame=quantity_by_fruit,
        categorical_columns="fruit",
        numeric_column="quantity",
    )
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Vertical bar plot - Label sort")
    ch.set_subtitle("Set `categorical_order_by` to sort by labels")
    ch.plot.bar(
        data_frame=quantity_by_fruit,
        categorical_columns="fruit",
        numeric_column="quantity",
        categorical_order_by="labels",
        categorical_order_ascending=True,
    )
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, y_axis_type="categorical")
    ch.set_title("Horizontal bar plot")
    ch.set_subtitle("Horizontal with color grouping")
    ch.plot.bar(
        data_frame=quantity_by_fruit,
        categorical_columns="fruit",
        numeric_column="quantity",
        color_column="fruit",
    )
    ch.show('png')

    ch = chartify.Chart(x_axis_type="categorical", blank_labels=True)
    ch.set_title("Vertical bar plot with labels")
    ch.set_subtitle("Hidden y-axis")
    ch.plot.bar(
        data_frame=quantity_by_fruit,
        categorical_columns="fruit",
        numeric_column="quantity",
        color_column="fruit",
    )
    ch.style.color_palette.reset_palette_order()
    ch.plot.text(
        data_frame=quantity_by_fruit,
        categorical_columns="fruit",
        numeric_column="quantity",
        text_column="quantity",
        color_column="fruit",
    )
    # Adjust the axis range to prevent clipping of the text labels.
    ch.axes.set_yaxis_range(0, 1200)
    ch.axes.hide_yaxis()
    ch.show('png')

    Grouped bar example.

    ch.plot.bar() docstring:
    Bar chart.

        Note:
            To change the orientation set x_axis_type or y_axis_type
            argument of the Chart object.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific color sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'values'.
                - 'values': Order categorical axis by the numerical
                    axis values. Default.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional): Sort order of the
                categorical axis. Default False.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    quantity_by_fruit_and_country = data.groupby(["fruit", "country"])["quantity"].sum().reset_index()
    print(quantity_by_fruit_and_country.head())
    
   fruit country  quantity
0  Apple      BR        57
1  Apple      CA       144
2  Apple      GB       177
3  Apple      JP        65
4  Apple      US       165

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Grouped bar chart")
    ch.set_subtitle("Pass a list to group by multiple factors. Color grouped by 'fruit'")
    ch.plot.bar(
        data_frame=quantity_by_fruit_and_country,
        categorical_columns=["fruit", "country"],
        numeric_column="quantity",
        color_column="fruit",
    )
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Grouped bar chart - Color groupings")
    ch.set_subtitle("Change color independent of the axis factors. Color grouped by 'country'")
    ch.plot.bar(
        data_frame=quantity_by_fruit_and_country,
        categorical_columns=["fruit", "country"],
        numeric_column="quantity",
        color_column="country",
    )
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Grouped bar chart - Group hierarchy order")
    ch.set_subtitle("Change chage order of 'categorical_column' list to switch grouping hierarchy.")
    ch.plot.bar(
        data_frame=quantity_by_fruit_and_country,
        categorical_columns=["country", "fruit"],
        numeric_column="quantity",
        color_column="country",
    )
    ch.axes.set_xaxis_tick_orientation("vertical")
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Grouped bar chart - Factor order")
    ch.set_subtitle("Change categorical order with 'categorical_order_by'.")
    ch.plot.bar(
        data_frame=quantity_by_fruit_and_country,
        categorical_columns=["country", "fruit"],
        numeric_column="quantity",
        color_column="country",
        categorical_order_by="labels",
        categorical_order_ascending=True,
    )
    ch.axes.set_xaxis_tick_orientation("vertical")
    ch.show('png')

Box-and-whisker plot.

        Note:
            To change the orientation set x_axis_type or y_axis_type
            argument of the Chart object.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional):
                List of values within the 'color_column' for
                    specific color sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'labels'.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional):
                Sort order of the categorical axis. Default True.
            outlier_marker (str, optional): Outlier marker type. Valid types:
                'asterisk', 'circle', 'circle_cross', 'circle_x', 'cross',
                'diamond', 'diamond_cross', 'hex', 'inverted_triangle',
                'square', 'square_x', 'square_cross', 'triangle',
                'x', '*', '+', 'o', 'ox', 'o+' Default 'circle'
            outlier_color (str, optional): Color name or hex value.
                See chartify.color_palettes.show() for available color names.
                Default 'black'
            outlier_alpha (float, optional): Alpha value. Default 0.3
            outlier_size (float, optional): Size of outlier markers.
                Default 15
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()
    

    ch = chartify.Chart(x_axis_type="categorical")
    ch.plot.boxplot(
        data_frame=quantity_by_fruit_and_country,
        categorical_columns=["fruit", "country"],
        numeric_column="quantity",
        color_column="country",
    )
    ch.set_title("Distribution of number of fruits by day")
    ch.set_subtitle("split by fruit type and country")
    ch.axes.set_xaxis_label("Fruit and country")
    ch.axes.set_yaxis_label("Distribution of number of fruits by day")
    ch.show('png')

Interval.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            lower_bound_column (str): Column name to plot on the
                numerical axis for the lower bound.
            upper_bound_column (str): Column name to plot on the
                numerical axis for the upper bound.
            middle_column (str, optional): Column name to plot on the
                numerical axis for the middle tick.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'values'.
                - 'values': Order categorical axis by the numerical
                    axis values. Default.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional): Sort order of the
                categorical axis. Default False.
            color (str): Color name or hex value.
                See chartify.color_palettes.show() for available color names.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()
    

    avg_price_with_interval = (
        data.groupby("fruit")["total_price"]
        .agg(["mean", "std", "count"])
        .assign(
            lower_ci=lambda x: x["mean"] - 1.96 * x["std"] / x["count"] ** 0.5,
            upper_ci=lambda x: x["mean"] + 1.96 * x["std"] / x["count"] ** 0.5,
        )
        .reset_index()
    )
    

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Interval plots")
    ch.set_subtitle("Represent variation. Optional `middle_column` to mark a middle point.")
    ch.plot.interval(
        data_frame=avg_price_with_interval,
        categorical_columns="fruit",
        lower_bound_column="lower_ci",
        upper_bound_column="upper_ci",
        middle_column="mean",
    )
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Combined interval plot & bar plot")
    ch.plot.bar(
        data_frame=avg_price_with_interval,
        categorical_columns="fruit",
        numeric_column="mean",
    )
    ch.plot.interval(
        data_frame=avg_price_with_interval,
        categorical_columns="fruit",
        lower_bound_column="lower_ci",
        upper_bound_column="upper_ci",
    )
    ch.show('png')

Lollipop chart.

        Note:
            To change the orientation set x_axis_type or y_axis_type
            argument of the Chart object.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional):
                List of values within the 'color_column' for
                    specific color sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'values'.
                - 'values': Order categorical axis by the numerical axis values.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional):
                Sort order of the categorical axis. Default False.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    quantity_by_fruit_and_country = data.groupby(["fruit", "country"])["quantity"].sum().reset_index()
    print(quantity_by_fruit_and_country.head())
    
   fruit country  quantity
0  Apple      BR        57
1  Apple      CA       144
2  Apple      GB       177
3  Apple      JP        65
4  Apple      US       165

    # Plot the data
    ch = chartify.Chart(blank_labels=True, y_axis_type="categorical")
    ch.set_title("Lollipop chart")
    ch.set_subtitle("Same options as bar plot")
    ch.plot.lollipop(
        data_frame=quantity_by_fruit_and_country,
        categorical_columns=["country", "fruit"],
        numeric_column="quantity",
        color_column="country",
    )
    ch.show('png')

Plot stacked bar chart.

        Note:
            - To change the orientation set x_axis_type or y_axis_type
            argument of the Chart object.
            - Stacked numeric values must be all positive or all negative.
            To plot both positive and negative values on the same chart
            call this method twice. Once for the positive values and
            once for the negative values.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            stack_column (str): Column name to group by on the stack dimension.
            normalize (bool, optional): Normalize numeric dimension for
                100% stacked bars. Default False.
            stack_order (list, optional): List of values within the
                'stack_column' dimension for specific stack sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'values'.
                - 'values': Order categorical axis by the numerical
                    axis values. Default.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional): Sort order
                of the categorical axis. Default False.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    quantity_by_fruit_and_country = data.groupby(["fruit", "country"])["quantity"].sum().reset_index()
    print(quantity_by_fruit_and_country.head())
    
   fruit country  quantity
0  Apple      BR        57
1  Apple      CA       144
2  Apple      GB       177
3  Apple      JP        65
4  Apple      US       165

    # Plot the data
    (
        chartify.Chart(blank_labels=True, x_axis_type="categorical")
        .set_title("Stacked bar chart")
        .set_subtitle("Stack columns by a categorical factor.")
        .plot.bar_stacked(
            data_frame=quantity_by_fruit_and_country,
            categorical_columns=["fruit"],
            numeric_column="quantity",
            stack_column="country",
            normalize=False,
        )
        .show('png')
    )

    (
        chartify.Chart(blank_labels=True, x_axis_type="categorical")
        .set_title("Grouped bar chart - Normalized")
        .set_subtitle("Set the 'normalize' parameter for 100% bars.")
        .plot.bar_stacked(
            data_frame=quantity_by_fruit_and_country,
            categorical_columns=["fruit"],
            numeric_column="quantity",
            stack_column="country",
            normalize=True,
        )
        .show('png')
    )

    # Get the ordered list of quantity by country to order the stacks.
    country_order = (
        quantity_by_fruit_and_country.groupby("country")["quantity"].sum().sort_values(ascending=False).index
    )
    (
        chartify.Chart(blank_labels=True, x_axis_type="categorical")
        .set_title("Grouped bar chart - Ordered stack")
        .set_subtitle("Change the order of the stack with `stack_order`.")
        .plot.bar_stacked(
            data_frame=quantity_by_fruit_and_country,
            categorical_columns=["fruit"],
            numeric_column="quantity",
            stack_column="country",
            normalize=True,
            stack_order=country_order,
        )
        .show('png')
    )
    

    # Add a column for labels.
    # Note: Null labels will not be added to the chart.
    quantity_by_fruit_and_country["label"] = np.where(
        quantity_by_fruit_and_country["country"].isin(["US", "CA"]),
        quantity_by_fruit_and_country["quantity"],
        None,
    )

    (
        chartify.Chart(blank_labels=True, x_axis_type="categorical")
        .set_title("Stacked bar with labels")
        .set_subtitle("")
        .plot.bar_stacked(
            data_frame=quantity_by_fruit_and_country,
            categorical_columns=["fruit"],
            numeric_column="quantity",
            stack_column="country",
            normalize=True,
            stack_order=country_order,
        )
        .plot.text_stacked(
            data_frame=quantity_by_fruit_and_country,
            categorical_columns=["fruit"],
            numeric_column="quantity",
            stack_column="country",
            text_column="label",
            normalize=True,
            stack_order=country_order,
            # Set the text color otherwise it will take
            # The next color in the color palette.
            text_color="white",
        )
        .show('png')
    )

Parallel coordinate plot.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific color sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'values'.
                - 'values': Order categorical axis by the numerical axis values.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional):
                Sort order of the categorical axis. Default False.
            line_dash (str, optional): Dash style for the line. One of:
                - 'solid'
                - 'dashed'
                - 'dotted'
                - 'dotdash'
                - 'dashdot'
            line_width (int, optional): Width of the line
            alpha (float): Alpha value
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    total_quantity_by_fruit_and_country = data.groupby(["fruit", "country"])["quantity"].sum().reset_index()
    print(total_quantity_by_fruit_and_country.head())
    
   fruit country  quantity
0  Apple      BR        57
1  Apple      CA       144
2  Apple      GB       177
3  Apple      JP        65
4  Apple      US       165

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="categorical")
    ch.set_title("Parallel coordinate charts")
    ch.set_subtitle("")
    ch.plot.parallel(
        data_frame=total_quantity_by_fruit_and_country,
        categorical_columns="fruit",
        numeric_column="quantity",
        color_column="country",
    )
    ch.show('png')

Scatter chart.

        Note:
            To change the orientation set x_axis_type or y_axis_type
            argument of the Chart object.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            categorical_columns (str or list): Column name to plot on
                the categorical axis.
            numeric_column (str): Column name to plot on the numerical axis.
            size_column (str, optional): Column name of numerical values
                to plot on the size dimension.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional):
                List of values within the 'color_column' for
                    specific color sort.
            categorical_order_by (str or array-like, optional):
                Dimension for ordering the categorical axis. Default 'count'.
                - 'count': Order categorical axis by the count of values.
                - 'labels': Order categorical axis by the categorical labels.
                - array-like object (list, tuple, np.array): New labels
                    to conform the categorical axis to.
            categorical_order_ascending (bool, optional):
                Sort order of the categorical axis. Default False.
            alpha (float): Alpha value.
            marker (str): marker type. Valid types:
                'asterisk', 'circle', 'circle_cross', 'circle_x', 'cross',
                'diamond', 'diamond_cross', 'hex', 'inverted_triangle',
                'square', 'square_x', 'square_cross', 'triangle',
                'x', '*', '+', 'o', 'ox', 'o+'
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()
    high_low = data.groupby(["fruit"])["unit_price"].agg(["max", "min"]).reset_index()
    print(high_low.head())
    
    fruit       max       min
0   Apple  1.244992  0.767347
1  Banana  0.326851  0.171751
2   Grape  2.516920  1.433120
3  Orange  0.632614  0.355687

    # Plot the data
    ch = chartify.Chart(blank_labels=True, y_axis_type="categorical")
    ch.set_title("Scatter plot with categorical y-axis")
    ch.plot.scatter(
        data_frame=high_low,
        categorical_columns="fruit",
        numeric_column="max",
        marker="circle",
    )
    ch.plot.scatter(
        data_frame=high_low,
        categorical_columns="fruit",
        numeric_column="min",
        marker="square",
    )
    ch.show('png')

Heatmap.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            x_column (str): Column name to plot on the x axis.
            y_column (str): Column name to plot on the y axis.
            color_column (str): Column name of numerical type to plot on
                the color dimension.
            text_column (str or None): Column name of the text labels.
            color_palette (str, chartify.ColorPalette): Color palette to
                apply to the heatmap.
                See chartify.color_palettes.show() for available color palettes.
            reverse_color_order (bool): Reverse order of the color palette.
            text_color (str): Color name or hex value.
                See chartify.color_palettes.show() for available color names.
            text_format: Python string formatting to apply to the text labels.
            color_value_min (float): Minimum value for the color palette.
                If None, will default to the min value of the
                color_column dimension.
            color_value_max (float): Maximum value for the color palette.
                If None, will default to the max value of the
                color_column dimension.
            color_value_range (int): The size of the range of colors in
                the color palette.
                A larger color range will result in greater variation
                among the cell colors.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    average_price_by_fruit_and_country = data.groupby(["fruit", "country"])["total_price"].mean().reset_index()
    

    # Plot the data
    (
        chartify.Chart(blank_labels=True, x_axis_type="categorical", y_axis_type="categorical")
        .plot.heatmap(
            data_frame=average_price_by_fruit_and_country,
            x_column="fruit",
            y_column="country",
            color_column="total_price",
            text_column="total_price",
            text_color="white",
        )
        .axes.set_xaxis_label("Fruit")
        .axes.set_yaxis_label("Country")
        .set_title("Heatmap")
        .set_subtitle("Plot numeric value grouped by two categorical values")
        .show('png')
    )

Kernel Density Estimate Plot.

        Args:
            data_frame (pandas.DataFrame): Data source for the plot.
            values_column (str): Column of numeric values.
            color_column (str, optional): Column name to group by on
                the color dimension.
            color_order (list, optional): List of values within the
                'color_column' for specific sorting of the colors.
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    print(data.head())
    
        date country   fruit  unit_price  quantity  total_price
0 2017-10-21      US  Banana    0.303711         4     1.214846
1 2017-05-30      JP  Banana    0.254109         4     1.016436
2 2017-05-21      CA  Banana    0.268635         4     1.074539
3 2017-09-18      BR   Grape    2.215277         2     4.430554
4 2017-12-08      US  Banana    0.308337         5     1.541687

    # Plot the data
    ch = chartify.Chart(blank_labels=True, y_axis_type="density")
    ch.set_title("Histogram")
    ch.set_subtitle("")
    ch.plot.histogram(data_frame=data, values_column="unit_price", bins=50)
    ch.show('png')

    ch = chartify.Chart(blank_labels=True, x_axis_type="density")
    ch.set_title("Horizontal histogram with grouping")
    ch.set_subtitle("")
    ch.plot.histogram(data_frame=data, values_column="unit_price", color_column="fruit")
    ch.show('png')

    KDE example
    

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    print(data.head())
    
        date country   fruit  unit_price  quantity  total_price
0 2017-10-21      US  Banana    0.303711         4     1.214846
1 2017-05-30      JP  Banana    0.254109         4     1.016436
2 2017-05-21      CA  Banana    0.268635         4     1.074539
3 2017-09-18      BR   Grape    2.215277         2     4.430554
4 2017-12-08      US  Banana    0.308337         5     1.541687

    # Plot the data
    ch = chartify.Chart(blank_labels=True, y_axis_type="density")
    ch.set_title("KDE plot")
    ch.plot.kde(data_frame=data, values_column="unit_price", color_column="fruit")
    ch.show('png')

    # Plot the data
    ch = chartify.Chart(blank_labels=True, y_axis_type="density")
    ch.set_title("KDE plot + Histogram")
    ch.plot.kde(data_frame=data, values_column="unit_price", color_column="fruit")
    ch.style.color_palette.reset_palette_order()
    ch.plot.histogram(
        data_frame=data,
        values_column="unit_price",
        color_column="fruit",
        method="density",
    )
    ch.show('png')

    Hexbin example
    

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    ch = chartify.Chart(blank_labels=True, x_axis_type="density", y_axis_type="density")
    ch.set_title("Hexbin")
    ch.plot.hexbin(
        data_frame=data,
        x_values_column="unit_price",
        y_values_column="quantity",
        size=0.2,
        orientation="pointytop",
    )
    ch.show('png')

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Sum price grouped by date
    price_by_date = data.groupby("date")["total_price"].sum().reset_index()  # Move 'date' from index to column
    price_by_date["triple_total_price"] = price_by_date["total_price"] * 3
    price_by_date = price_by_date.sort_values("date")
    print(price_by_date.head())
    
        date  total_price  triple_total_price
0 2017-01-10     1.808778            5.426334
1 2017-01-12     0.829621            2.488864
2 2017-01-22     1.998476            5.995427
3 2017-01-27     1.390764            4.172292
4 2017-01-28     2.658465            7.975394

    # Initialize chart with second_y_axis=True
    ch = chartify.Chart(
        blank_labels=True,
        x_axis_type="datetime",
        y_axis_type="linear",
        second_y_axis=True,
    )
    ch.set_title("Second Y axis")
    # Plot the first axis
    ch.plot.line(data_frame=price_by_date, x_column="date", y_column="total_price")
    ch.axes.set_yaxis_range(0, 50)
    ch.axes.set_yaxis_label("First axis label")
    ch.axes.set_yaxis_tick_format("0a")

    # Plot the second axis
    ch.second_axis.axes.set_yaxis_range(0, 80)
    ch.second_axis.axes.set_yaxis_label("Second axis label")
    ch.second_axis.plot.line(
        # Data must be sorted by x column
        data_frame=price_by_date,
        x_column="date",
        y_column="triple_total_price",
    )
    ch.second_axis.axes.set_yaxis_tick_format("0.0")
    ch.show('png')

    import chartify

    # Generate example data
    data = chartify.examples.example_data()
    total_by_fruit_and_country = data.groupby(["fruit", "country"])["quantity"].sum().reset_index()
    print(total_by_fruit_and_country.head())
    
   fruit country  quantity
0  Apple      BR        57
1  Apple      CA       144
2  Apple      GB       177
3  Apple      JP        65
4  Apple      US       165

    ch = chartify.RadarChart(True, layout="slide_50%")
    ch.set_title("Radar Area Chart")
    ch.set_subtitle("Each vertex plotted counterclockwise starting from top")
    ch.plot.text(
        total_by_fruit_and_country.groupby("country")["quantity"].max().reset_index(),
        "quantity",
        text_column="country",
        text_align="center",
    )
    ch.plot.area(total_by_fruit_and_country, "quantity", color_column="fruit")
    ch.axes.hide_yaxis()
    ch.axes.hide_xaxis()
    ch.set_legend_location("outside_bottom")
    ch.show('png')

    import chartify
    from itertools import product

    # Generate example data
    data = chartify.examples.example_data()

    total_by_country = data.groupby(["country"])["quantity"].sum().reset_index()
    countries = total_by_country.country.unique()
    grid_values = product(range(0, 1200, 200), countries)
    grid_df = pd.DataFrame.from_records(grid_values, columns=["quantity", "country"])
    quantity_label = grid_df.groupby("quantity")[["quantity"]].min().reset_index(drop=True)

    ch = chartify.RadarChart(True, layout="slide_50%")
    ch.set_title("Radar Radius Chart")

    ch.style.set_color_palette("categorical", ["grey"])
    # Plot quantity labels
    ch.plot.text(
        quantity_label,
        "quantity",
        text_column="quantity",
        color_column="quantity",
        font_size="8pt",
    )
    # Plot perimeter grid
    ch.plot.perimeter(grid_df, radius_column="quantity", color_column="quantity", line_width=1)
    ch.style.set_color_palette("categorical")

    # Plot text labels
    ch.plot.text(total_by_country, "quantity", text_column="country", text_align="center")
    ch.style.color_palette.reset_palette_order()
    # Plot the radius
    ch.plot.radius(total_by_country, "quantity")
    ch.axes.hide_yaxis()
    ch.axes.hide_xaxis()
    ch.set_legend_location(None)
    ch.show('png')

    Chart label examples
    

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    apple_prices = data[data["fruit"] == "Apple"].groupby(["quantity"])["unit_price"].mean().reset_index()
    # Plot the data with method chaining
    (
        chartify.Chart(blank_labels=True)
        .plot.scatter(apple_prices, "quantity", "unit_price")
        .set_title("Quantity decreases as price increases. <--  Use title for takeaway.")
        .set_subtitle("Quantity vs. Price. <-- Use subtitle for data description.")
        .axes.set_xaxis_label("Quantity per sale (Apples)")
        .axes.set_yaxis_label("Price ($)")
        .axes.set_yaxis_tick_format("$0.00")
        .show('png')
    )

    Axis type examples
    

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Plot the data
    (
        chartify.Chart(blank_labels=True, x_axis_type="log")
        .plot.scatter(data_frame=data, x_column="total_price", y_column="quantity")
        .set_subtitle("Set axis type for auto handling of categorical, datetime, linear, or log values.")
        .set_title("Axis Type")
        .show('png')
    )

    import chartify

    # Generate example data
    data = chartify.examples.example_data()
    data["%_sales"] = data["quantity"] / data["quantity"].sum()

    # Plot the data
    (
        chartify.Chart(blank_labels=True)
        .plot.scatter(data_frame=data, x_column="%_sales", y_column="unit_price")
        .axes.set_yaxis_tick_format("$0.00")
        .axes.set_xaxis_tick_format("0.00%")
        .set_subtitle("Format ticks on either axis to set units or precision")
        .set_title("Axis tick format")
        .show('png')
    )

    import pandas as pd
    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.plot.scatter(data, "date", "quantity")
    ch.set_title("Axis tick values")
    ch.set_subtitle("Pass a list of values or use pd.date_range for datetime axes")
    # Use pd.date_range to generate a range of dates
    # at the start of each month
    ch.axes.set_xaxis_tick_values(pd.date_range("2017-01-01", "2018-01-01", freq="MS"))
    ch.axes.set_yaxis_tick_values(list(range(0, 8, 2)))
    ch.show('png')

    Line example
    

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Plot the data
    (
        chartify.Chart(blank_labels=True)
        .plot.scatter(data_frame=data, x_column="unit_price", y_column="total_price")
        .callout.line(2)  # Callout horizontal line
        .callout.line(1, "height")  # Callout vertical line
        .set_title("Line callout")
        .set_subtitle("Callout lines on either axis")
        .show('png')
    )

Add box callout to the chart.

        Args:
            top (numeric, optional): Top edge of the box.
            bottom (numeric, optional): Bottom edge of the box.
            left (numeric, optional): Left edge of the box.
            right (numeric, optional): Right edge of the box.
            alpha (float, optional): 0.2
            color (str): Color name or hex value.
                See chartify.color_palettes.show() for available color names.

        Note:
            The box will extend to the edge if the corresponding position
            argument is omitted.

        Returns:
            Current chart object
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Plot the data
    (
        chartify.Chart(blank_labels=True)
        .plot.scatter(data_frame=data, x_column="total_price", y_column="unit_price")
        .callout.box(top=1, bottom=-1, color="red")
        .callout.box(top=2, left=4, color="blue")
        .callout.box(bottom=2, right=3, color="green")
        .set_title("Shaded area callout")
        .set_subtitle("Highlight notable areas of your chart")
        .show('png')
    )

Add text callout to the chart.

        Note:
            Use `
` within text for newlines.
        Args:
            x (numeric): x location of the text.
            y (numeric, optional): y location of the text.
            text_color (str): Color name or hex value.
                See chartify.color_palettes.show() for available color names.
            text_align (str: 'left', 'right', 'center'): Text alignment.
            font_size (str): Font size.
            angle (int, 0 to 360): Angle in degrees from horizontal. Default: 0

        Returns:
            Current chart object
        

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.plot.scatter(data_frame=data, x_column="unit_price", y_column="total_price")
    ch.callout.text("Description of what is\ngoing on in this chart!", 0, 6)
    ch.set_title("Text callout")
    ch.set_subtitle("Add narrative to your chart.")
    ch.show('png')

    Color palette
    

    import numpy as np
    import pandas as pd
    import chartify

    # Generate example data
    data = pd.DataFrame({"x": list(range(100))})
    data["y"] = data["x"] * np.random.normal(size=100)
    data["z"] = np.random.choice([2, 4, 5], size=100)
    data["country"] = np.random.choice(["US", "GB", "CA", "JP", "BR"], size=100)

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.style.set_color_palette(palette_type="categorical")
    ch.plot.scatter(data_frame=data, x_column="x", y_column="y", color_column="country")
    ch.set_title("Categorical color palette type")
    ch.set_subtitle("Default palette type. Use to differentiate categorical series.")
    ch.show('png')
    

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.style.set_color_palette(
        palette_type="categorical",
    )
    ch.plot.scatter(data_frame=data, x_column="x", y_column="y", color_column="country")
    ch.set_title("Pass 'palette' parameter to .set_color_palette() to change palette colors.")
    ch.set_subtitle("")
    ch.show('png')

    Color palette
    

    import numpy as np
    import pandas as pd
    import chartify

    # Generate example data
    data = pd.DataFrame({"x": list(range(100))})
    data["y"] = data["x"] * np.random.normal(size=100)
    data["z"] = np.random.choice([2, 4, 5], size=100)
    data["country"] = np.random.choice(["US", "GB", "CA", "JP", "BR"], size=100)

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.style.set_color_palette("accent", accent_values=["US", "CA"])
    ch.plot.scatter(
        data_frame=data,
        x_column="x",
        y_column="y",
        size_column="z",
        color_column="country",
    )
    ch.set_title("Accent color palette")
    ch.set_subtitle("Highlight specific color values or assign specific colors to values.")
    ch.show('png')

    Color palette sequential
    

    import numpy as np
    import pandas as pd
    import chartify

    data = pd.DataFrame({"time": pd.date_range("2015-01-01", "2018-01-01")})
    n_days = len(data)
    data["1st"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days)
    data["2nd"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 200
    data["3rd"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 500
    data["4th"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 700
    data["5th"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 800
    data["6th"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 1000
    data = pd.melt(
        data,
        id_vars=["time"],
        value_vars=data.columns[1:],
        value_name="y",
        var_name=["grouping"],
    )

    # Plot the data
    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.style.set_color_palette(palette_type="sequential")
    ch.plot.line(
        data_frame=data.sort_values("time"),
        x_column="time",
        y_column="y",
        color_column="grouping",
    )
    ch.set_title("Sequential color palette type")
    ch.set_subtitle("Palette type for sequential ordered dimensions")
    ch.show('png')

    Color palette sequential
    

    import numpy as np
    import pandas as pd
    import chartify

    data = pd.DataFrame({"time": pd.date_range("2015-01-01", "2018-01-01")})
    n_days = len(data)
    data["Very Unlikely"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days)
    data["Unlikely"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 200
    data["Neutral"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 500
    data["Likely"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 700
    data["Very Likely"] = np.array(list(range(n_days))) + np.random.normal(0, 10, size=n_days) + 800
    data = pd.melt(
        data,
        id_vars=["time"],
        value_vars=data.columns[1:],
        value_name="y",
        var_name=["grouping"],
    )

    # Plot the data

    ch = chartify.Chart(blank_labels=True, x_axis_type="datetime")
    ch.style.set_color_palette(palette_type="diverging")
    color_order = ["Very Unlikely", "Unlikely", "Neutral", "Likely", "Very Likely"]
    ch.plot.line(
        data_frame=data.sort_values("time"),
        x_column="time",
        y_column="y",
        color_column="grouping",
        color_order=color_order,
    )  # Your data must be sorted
    ch.set_title("Diverging color palette type")
    ch.set_subtitle("Palette type for diverging ordered dimensions")
    ch.show('png')

    Custom color palette
    

    import chartify

    # Generate example data
    data = chartify.examples.example_data()

    # Create a new custom palette
    chartify.color_palettes.create_palette(
        colors=["#ff0000", "yellow", "purple", "orange"],
        palette_type="categorical",
        name="custom palette",
    )

    ch = chartify.Chart(blank_labels=True)
    # Apply the custom palette
    ch.style.set_color_palette("categorical", "custom palette")
    ch.plot.scatter(
        data_frame=data,
        x_column="unit_price",
        y_column="quantity",
        color_column="fruit",
    )
    ch.set_title("Custom color palette")
    ch.show('png')

    import numpy as np
    import pandas as pd
    import chartify

    # Generate example data
    data = pd.DataFrame({"Price": list(range(100))})
    data["Demand"] = 100 + -0.5 * data["Price"] + np.random.normal(size=100)

    layouts = ["slide_100%", "slide_75%", "slide_50%", "slide_25%"]

    def display_layout(layout):
        (
            chartify.Chart(layout=layout)  # Assign the layout when instantiating the chart.
            .plot.scatter(data_frame=data, x_column="Price", y_column="Demand")
            .set_title("Slide layout: '{}'".format(layout))
            .set_subtitle("Demand vs. Price.")
            .set_source_label("")
            .axes.set_xaxis_label("Demand (# Users)")
            .axes.set_yaxis_label("Price ($)")
            .show('png')
        )

    [display_layout(layout) for layout in layouts]

Show the chart.

        Args:
            format (str):
                - 'html': Output chart as HTML.
                    Renders faster and allows for interactivity.
                    Charts saved as HTML in a Jupyter notebooks
                    WILL NOT display on Github.
                    Logos will not display on HTML charts.
                    Recommended when drafting plots.

                - 'png': Output chart as PNG.
                    Easy to copy+paste into slides.
                    Will render logos.
                    Recommended when the plot is in a finished state.

                - 'svg': Output as SVG.
        

    import numpy as np
    import pandas as pd
    import chartify

    # Generate example data
    data = pd.DataFrame({"x": list(range(100))})
    data["y"] = data["x"] * np.random.normal(size=100)
    data["z"] = np.random.choice([2, 4, 5], size=100)
    data["country"] = np.random.choice(["US", "GB", "CA", "JP", "BR"], size=100)

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.plot.scatter(
        data_frame=data,
        x_column="x",
        y_column="y",
        size_column="z",
        color_column="country",
    )
    ch.set_title("ch.show(): Faster rendering with HTML. Recommended while drafting.")
    ch.set_subtitle("No watermark. Does not display on github.")
    ch.show("html")  # Show with HTML

    ch.set_title('ch.show("png"): Return a png file for easy copying into slides')
    ch.set_subtitle("Will display on github.")
    ch.show("png")  # Show with PNG

WARNING:bokeh.core.validation.check:W-1000 (MISSING_RENDERERS): Plot has no renderers: figure(id='p17279', ...)

Save the chart.

        Args:
            filename (str): Name of output file.
            format (str):
                - 'html': Output chart as HTML.
                    Renders faster and allows for interactivity.
                    Charts saved as HTML in a Jupyter notebook WILL NOT display
                    on Github.
                    Logos will not display on HTML charts.
                    Recommended when drafting plots.

                - 'png': Output chart as PNG.
                    Easy to paste into google slides.
                    Recommended when the plot is in a finished state.
                    Will render logos.

                - 'svg': Output as SVG.
        

    import chartify

    # Plot the data
    ch = chartify.Chart(blank_labels=True)
    ch.set_title("ch.show(): Save chart to html, png, or svg.")
    ch.save("saved_chart_example.html", format="html")  # Save to html

Saved to saved_chart_example.html