Histogram¶

This section showcases the histogram chart. It contains examples of how to create the histogram using the datachart.charts.Histogram function.

The examples sequentially build on each other, going from simple to complex.

As mentioned above, the histogram are created using the Histogram function found in the datachart.charts module. Let's import it:

In [1]:

from datachart.charts import Histogram

from datachart.charts import Histogram

Double figure generation avoidence

To avoid double figure generation, the Histogram function is preceded by the _ = operator. The double figures are generated because Histogram returns the plt.Figure object, which is then used to save the figure locally.

Histogram Input Attributes¶

The Histogram function accepts keyword arguments for chart configuration. The main argument is data, which contains the data points. For a single histogram, data is a list of dictionaries. For multiple histograms, data is a list of lists.

Histogram(
    data=[{                                             # A list of histogram data points (or list of lists for multiple charts)
        "x":     Union[int, float],                     # The x-axis value
    }],
    style={                                             # The style of the histogram (optional)
        "plot_hist_color":      Union[str, None],       # The color of the histogram
        "plot_hist_alpha":      Union[float, None],     # The transparency of the histogram
        "plot_hist_zorder":     Union[int, float, None], # The z-order of the histogram
        "plot_hist_fill":       Union[str, None],       # The fill color of the histogram
        "plot_hist_hatch":      Union[HATCH_STYLE, None], # The hatch pattern
        "plot_hist_type":       Union[str, None],       # The type of the histogram
        "plot_hist_align":      Union[str, None],       # The alignment of the histogram
        "plot_hist_edge_width": Union[int, float, None], # The width of the histogram edge
        "plot_hist_edge_color": Union[str, None],       # The color of the histogram edge
    },
    subtitle=Optional[str],                             # The subtitle of the chart (or list for multiple charts)
    title=Optional[str],                                # The title of the chart
    xlabel=Optional[str],                               # The x-axis label
    ylabel=Optional[str],                               # The y-axis label

    xticks=Optional[List[Union[int, float]]],           # the x-axis ticks
    xticklabels=Optional[List[str]],                    # the x-axis tick labels (must be same length as xticks)
    xtickrotate=Optional[int],                          # the x-axis tick labels rotation
    yticks=Optional[List[Union[int, float]]],           # the y-axis ticks
    yticklabels=Optional[List[str]],                    # the y-axis tick labels (must be same length as yticks)
    ytickrotate=Optional[int],                          # the y-axis tick labels rotation

    vlines=Optional[Union[dict, List[dict]]],           # the vertical lines
    hlines=Optional[Union[dict, List[dict]]],           # the horizontal lines
)

For more details, see the datachart.typings.HistogramChartAttrs type.

Single Histogram Chart¶

In this part, we show how to create a single histogram chart using the Histogram function.

Let us first import the necessary libraries:

In [2]:

import random

import random

Basic example. Let us first create a basic histogram chart showing a random distribution.

The following example shows how only the data argument is required to draw the histogram chart.

In [3]:

NUM_OF_POINTS = 100

NUM_OF_POINTS = 100

In [4]:

chart_data = [{"x": 100 * idx * random.random()} for idx in range(NUM_OF_POINTS)]

chart_data = [{"x": 100 * idx * random.random()} for idx in range(NUM_OF_POINTS)]

In [5]:

_ = Histogram(
    # add the data to the chart
    data=chart_data
)

_ = Histogram( # add the data to the chart data=chart_data )

Chart title and axis labels¶

To add the chart title and axis labels, simply add the title, xlabel and ylabel attributes.

In [6]:

_ = Histogram(
    data=chart_data,
    # add the title
    title="Title",
    # add the x and y axis labels
    xlabel="the global x-axis label",
    ylabel="the global y-axis label"
)

_ = Histogram( data=chart_data, # add the title title="Title", # add the x and y axis labels xlabel="the global x-axis label", ylabel="the global y-axis label" )

Number of bins¶

The histogram bins the values into a number of bins. By default, the number of bins is 20.

To change the number of bins, simply modify the num_bins attribute.

In [7]:

_ = Histogram(
    data=chart_data,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    # change the number of bins
    num_bins=40
)

_ = Histogram( data=chart_data, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", # change the number of bins num_bins=40 )

Figure size and grid¶

To change the figure size, simply add the figsize attribute. The figsize attribute can be a tuple (width, height), values are in inches. The datachart package provides a datachart.constants.FIG_SIZE constant, which contains some of the predefined figure sizes.

To add the grid, simply add the show_grid attribute. The possible options are:

Option	Description
"both"	shows both the x-axis and the y-axis gridlines.
"x"	shows only the x-axis grid lines.
"y"	shows only the y-axis grid lines.

Again, datachart provides a datachart.constants.SHOW_GRID constant, which contains the supported options.

In [8]:

from datachart.constants import FIG_SIZE, SHOW_GRID

from datachart.constants import FIG_SIZE, SHOW_GRID

In [9]:

_ = Histogram(
    data=chart_data,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    # add to determine the figure size
    figsize=FIG_SIZE.A4_NARROW,
    # add to show the grid lines
    show_grid=SHOW_GRID.Y,
    num_bins=40,
)

_ = Histogram( data=chart_data, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", # add to determine the figure size figsize=FIG_SIZE.A4_NARROW, # add to show the grid lines show_grid=SHOW_GRID.Y, num_bins=40, )

Histogram style¶

To change a single histogram style simply add the style attribute with the corresponding attributes. The supported attributes are shown in the datachart.typings.HistStyleAttrs type, which contains the following attributes:

Attribute	Description
"plot_hist_color"	The color of the histogram (hex color code).
"plot_hist_alpha"	The alpha of the histogram (how visible the histogram is).
"plot_hist_zorder"	The zorder of the histogram.
"plot_hist_fill"	The fill color of the histogram (hex color code).
"plot_hist_hatch"	The hatch pattern of the histogram.
"plot_hist_type"	The type of the histogram.
"plot_hist_align"	The alignment of the histogram.
"plot_hist_edge_width"	The width of the histogram edge.
"plot_hist_edge_color"	The color of the histogram edge (hex color code).

Again, to help with the style settings, the datachart.constants module contains the following constants:

Constant	Description
datachart.constants.HATCH_STYLE	The hatch pattern of the histogram.
datachart.constants.HISTOGRAM_TYPE	The type of the histogram.

In [10]:

from datachart.constants import HISTOGRAM_TYPE, HATCH_STYLE

from datachart.constants import HISTOGRAM_TYPE, HATCH_STYLE

In [11]:

_ = Histogram(
    data=chart_data,
    # define the style of the histogram
    style={
        "plot_hist_hatch": HATCH_STYLE.DIAGONAL,
        "plot_hist_fill": True,
        "plot_hist_edge_width": 1,
        "plot_hist_edge_color": "#08519c",
        "plot_hist_type": HISTOGRAM_TYPE.STEP,
    },
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    num_bins=40,
)

_ = Histogram( data=chart_data, # define the style of the histogram style={ "plot_hist_hatch": HATCH_STYLE.DIAGONAL, "plot_hist_fill": True, "plot_hist_edge_width": 1, "plot_hist_edge_color": "#08519c", "plot_hist_type": HISTOGRAM_TYPE.STEP, }, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, num_bins=40, )

Histogram orientation¶

To change the orientation of the histograms, simply add the orientation attribute, which supports the following values:

Value	Description
"horizontal"	The histogram are horizontal.
"vertical"	The histogram are vertical.

Again, to help with the style settings, the datachart.constants module contains the following constants:

Constant	Description
datachart.constants.ORIENTATION	The orientation of the histogram.

In [12]:

from datachart.constants import ORIENTATION

from datachart.constants import ORIENTATION

In [13]:

_ = Histogram(
    data=chart_data,
    style={
        "plot_hist_hatch": HATCH_STYLE.DIAGONAL,
        "plot_hist_fill": True,
        "plot_hist_edge_width": 1,
        "plot_hist_edge_color": "#08519c",
        "plot_hist_type": HISTOGRAM_TYPE.STEP,
    },
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    # change the grid to match the change in orientation
    show_grid=SHOW_GRID.X,
    # change the orientation of the bars
    orientation=ORIENTATION.HORIZONTAL,
    num_bins=40,
)

_ = Histogram( data=chart_data, style={ "plot_hist_hatch": HATCH_STYLE.DIAGONAL, "plot_hist_fill": True, "plot_hist_edge_width": 1, "plot_hist_edge_color": "#08519c", "plot_hist_type": HISTOGRAM_TYPE.STEP, }, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, # change the grid to match the change in orientation show_grid=SHOW_GRID.X, # change the orientation of the bars orientation=ORIENTATION.HORIZONTAL, num_bins=40, )

Adding vertical and horizontal lines¶

Adding vertical lines. Use the vlines argument with the datachart.typings.VLinePlotAttrs typing, which is either a dict or a List[dict] where each dictionary contains some of the following attributes:

{
  "x":    Union[int, float],                 # The x-axis value
  "ymin": Optional[Union[int, float]],       # The minimum y-axis value
  "ymax": Optional[Union[int, float]],       # The maximum y-axis value
  "style": {                                 # The style of the line (optional)
    "plot_vline_color": Optional[str],       # The color of the line (hex color code)
    "plot_vline_style": Optional[LineStyle], # The line style (solid, dashed, etc.)
    "plot_vline_width": Optional[float],     # The width of the line
    "plot_vline_alpha": Optional[float],     # The alpha of the line (how visible the line is)
  },
  "label": Optional[str],                    # The label of the line
}

Adding horizontal lines. Use the hlines argument with the datachart.typings.HLinePlotAttrs typing, which is either a dict or a List[dict] where each dictionary contains some of the following attributes:

{
  "y":    Union[int, float],                 # The y-axis value
  "xmin": Optional[Union[int, float]],       # The minimum x-axis value
  "xmax": Optional[Union[int, float]],       # The maximum x-axis value
  "style": {                                 # The style of the line (optional)
    "plot_hline_color": Optional[str],       # The color of the line (hex color code)
    "plot_hline_style": Optional[LineStyle], # The line style (solid, dashed, etc.)
    "plot_hline_width": Optional[float],     # The width of the line
    "plot_hline_alpha": Optional[float],     # The alpha of the line (how visible the line is)
  },
  "label": Optional[str],                    # The label of the line
}

To add vertical and horizontal lines, simply add the vlines and hlines arguments.

In [14]:

from datachart.constants import LINE_STYLE

from datachart.constants import LINE_STYLE

In [15]:

_ = Histogram(
    data=chart_data,
    # add a list of vertical lines
    vlines=[
        {
            "x": 2000 * i,
            "style": {
                "plot_vline_color": "green",
                "plot_vline_style": LINE_STYLE.SOLID,
                "plot_vline_width": 1,
            },
        }
        for i in range(1, 4)
    ],
    # add a list of horizontal lines
    hlines={
        "y": 6,
        "style": {
            "plot_hline_color": "red",
            "plot_hline_style": LINE_STYLE.DASHED,
            "plot_hline_width": 2,
            "plot_hline_alpha": 0.5,
        },
    },
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    num_bins=40,
)

_ = Histogram( data=chart_data, # add a list of vertical lines vlines=[ { "x": 2000 * i, "style": { "plot_vline_color": "green", "plot_vline_style": LINE_STYLE.SOLID, "plot_vline_width": 1, }, } for i in range(1, 4) ], # add a list of horizontal lines hlines={ "y": 6, "style": { "plot_hline_color": "red", "plot_hline_style": LINE_STYLE.DASHED, "plot_hline_width": 2, "plot_hline_alpha": 0.5, }, }, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, num_bins=40, )

Multiple Histogram Charts¶

To create multiple histograms, pass a list of lists to the data argument. Each inner list represents the data for one histogram. Per-chart attributes like subtitle and style can be passed as lists, where each element corresponds to a chart.

Multiple charts pattern

For multiple charts, data becomes a list of lists, and per-chart attributes like subtitle and style become lists where each element applies to the corresponding chart.

In [16]:

# the data is now a list of lists (one per chart)
multi_chart_data = [
    [{"x": 100 * idx * random.random()} for idx in range(NUM_OF_POINTS)]
    for _ in range(2)
]

# the data is now a list of lists (one per chart) multi_chart_data = [ [{"x": 100 * idx * random.random()} for idx in range(NUM_OF_POINTS)] for _ in range(2) ]

In [17]:

_ = Histogram(
    # use a list of lists to define multiple histograms
    data=multi_chart_data,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    num_bins=40,
)

_ = Histogram( # use a list of lists to define multiple histograms data=multi_chart_data, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, num_bins=40, )

Sub-chart subtitles¶

We can name each chart by passing a list of subtitles to the subtitle argument. In addition, to help with discerning which chart is which, use the show_legend argument to show the legend of the charts.

In [18]:

# define subtitles for each chart
chart_subtitles = [f"Histogram {idx+1}" for idx in range(len(multi_chart_data))]

# define subtitles for each chart chart_subtitles = [f"Histogram {idx+1}" for idx in range(len(multi_chart_data))]

In [19]:

_ = Histogram(
    data=multi_chart_data,
    # add a subtitle to each chart
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    num_bins=40,
    # show the legend
    show_legend=True,
)

_ = Histogram( data=multi_chart_data, # add a subtitle to each chart subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, num_bins=40, # show the legend show_legend=True, )

Subplots¶

To draw multiple charts in each subplot, simply add the subplots attribute. The chart's subtitle are then added at the top of each subplot, while the title, xlabel and ylabel are positioned to be global for all charts.

In [20]:

_ = Histogram(
    data=multi_chart_data,
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    num_bins=40,
    # show each chart in its own subplot
    subplots=True,
)

_ = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, num_bins=40, # show each chart in its own subplot subplots=True, )

Sharing the x-axis and/or y-axis across subplots¶

To share the x-axis and/or y-axis across subplots, simply add the sharex and/or sharey attributes, which are boolean values that specify whether to share the axis across all subplots.

In [21]:

_ = Histogram(
    data=multi_chart_data,
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    num_bins=40,
    subplots=True,
    # share the x-axis across subplots
    sharex=True,
    # share the y-axis across subplots
    sharey=True,
)

_ = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, num_bins=40, subplots=True, # share the x-axis across subplots sharex=True, # share the y-axis across subplots sharey=True, )

Subplot orientation¶

The orientation attribute can be used to change the orientation of all subplots.

In [22]:

_ = Histogram(
    data=multi_chart_data,
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    num_bins=40,
    subplots=True,
    sharex=True,
    sharey=True,
    # change the grid to match the change in orientation
    show_grid=SHOW_GRID.X,
    # change the orientation of the histogram
    orientation=ORIENTATION.HORIZONTAL,
)

_ = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, num_bins=40, subplots=True, sharex=True, sharey=True, # change the grid to match the change in orientation show_grid=SHOW_GRID.X, # change the orientation of the histogram orientation=ORIENTATION.HORIZONTAL, )

Histogram Views¶

Density distribution view¶

To show the histograms as a density distribution, simply add the show_density attribute. This will scale the histogram bars to represent the density of the data, instead of the frequency of the data. Thus, the y-axis (or x-axis in horizontal orientation) will be between 0 and 1.

In [23]:

_ = Histogram(
    data=multi_chart_data,
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    subplots=True,
    sharex=True,
    sharey=True,
    num_bins=40,
    # shows the density of the data
    show_density=True,
)

_ = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, subplots=True, sharex=True, sharey=True, num_bins=40, # shows the density of the data show_density=True, )

Cumulative distribution view¶

To show the histograms as a cumulative distribution, simply add the show_cumulative attribute. This will show the cumulative distribution of the data, i.e. the height of each bar will be the sum of the previous bars.

In [24]:

_ = Histogram(
    data=multi_chart_data,
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.Y,
    subplots=True,
    sharex=True,
    sharey=True,
    num_bins=40,
    # shows the cumulative distribution
    show_cumulative=True,
)

_ = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.Y, subplots=True, sharex=True, sharey=True, num_bins=40, # shows the cumulative distribution show_cumulative=True, )

Cumulative & density distribution view¶

One can also combine the show_density and show_cumulative attributes. This will show the density of the data and the cumulative distribution of the data.

In [25]:

_ = Histogram(
    data=multi_chart_data,
    subtitle=chart_subtitles,
    title="Title",
    xlabel="the global x-axis label",
    ylabel="the global y-axis label",
    figsize=FIG_SIZE.A4_NARROW,
    show_grid=SHOW_GRID.X,
    subplots=True,
    sharex=True,
    sharey=True,
    num_bins=40,
    # shows the density of the data
    show_density=True,
    # shows the cumulative distribution
    show_cumulative=True,
)

_ = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title="Title", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.X, subplots=True, sharex=True, sharey=True, num_bins=40, # shows the density of the data show_density=True, # shows the cumulative distribution show_cumulative=True, )

Axis scales¶

The user can change the axis scale using the scalex and scaley attributes. The supported scale options are:

Options	Description
"linear"	The linear scale.
"log"	The log scale.
"symlog"	The symmetric log scale.
"asinh"	The asinh scale.

Again, to help with the options settings, the datachart.constants module contains the following constants:

Constant	Description
datachart.constants.SCALE	The axis options.

In [26]:

from datachart.constants import SCALE

from datachart.constants import SCALE

To showcase the supported scales, we iterate through all of the scales options.

In [27]:

for scale in [SCALE.LINEAR, SCALE.LOG, SCALE.SYMLOG, SCALE.ASINH]:
    figure = Histogram(
        data=multi_chart_data,
        subtitle=chart_subtitles,
        title=f"Graph showcasing the '{scale}' scale",
        xlabel="the global x-axis label",
        ylabel="the global y-axis label",
        figsize=FIG_SIZE.A4_NARROW,
        show_grid=SHOW_GRID.X,
        subplots=True,
        sharex=True,
        sharey=True,
        num_bins=40,
        # set the scale of the y axis
        scaley=scale,
    )

for scale in [SCALE.LINEAR, SCALE.LOG, SCALE.SYMLOG, SCALE.ASINH]: figure = Histogram( data=multi_chart_data, subtitle=chart_subtitles, title=f"Graph showcasing the '{scale}' scale", xlabel="the global x-axis label", ylabel="the global y-axis label", figsize=FIG_SIZE.A4_NARROW, show_grid=SHOW_GRID.X, subplots=True, sharex=True, sharey=True, num_bins=40, # set the scale of the y axis scaley=scale, )

Saving the Chart as an Image¶

To save the chart as an image, use the datachart.utils.save_figure function.

In [28]:

from datachart.utils import save_figure

from datachart.utils import save_figure

In [29]:

save_figure(figure, "./fig_histogram.png", dpi=300)

save_figure(figure, "./fig_histogram.png", dpi=300)

The figure should be saved in the current working directory.