Data Science Visualization -Heatmap

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What is a Heatmap?

A heat map (or heatmap) is a graphical representation of data where the individual values contained in a matrix are represented as colors.

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Heap maps originated in 2D displays of the values in a data matrix.

  • Larger values were represented by small dark gray or black squares
  • Smaller values were represented by small lighter squares

Different Kinds of Heatmaps:

  • Web heatmaps have been used for displaying area of a Web page most frequently scanned by visitors. There are severall different types of heatmaps, including:
    • Scroll maps
    • Hover mapping
    • Click mapping
    • Attention mapping
  • Biology heatmaps are typically used in molecular biology to represent the level of expression of many genes across a number of comparable samples as they are obtained from DNA microarrays.
  • The treemap is a 2D hierarchical partitioning of data that visually resembles a heatmap.
  • A density function visualization is a heatmap for representing the density of dots in a map. It enables one to perceive density of points independently of the zoom factor.

When to Use a Heatmap

Heatmaps are perfect for representing data where a defined boundary is relevant to the data.

Here are a few scenarios where having heatmap data handy can help you quickly make better decisions:

  • Website reedesign
  • A/B testing
  • Content marketing
  • …

The Reasons Why We Have to Use It

Heatmap data can help you to quickly see:

  • Which headlines draw visitors in and make them click
  • Which images attract attention and what people do about it – you’ll be surprised how many try to click the image
  • What distracts visitors from your core content (Many first-time heat map users are shocked at how differently their users navigate the site compared with their expectations.)
  • Whether visitors see your email opt-in box (Low opt-ins and click-throughs can be due to something as simple as opt-in boxes that don’t stand out. Heatmaps make that clear and obvious so you can fix it.)

You can also use a heat map to find out:

  • Whether your navigation is working (Do users know it’s there?)
  • If people can locate search options easily
  • Whether visitors are reading your content and how much of it (This is a huge use case for scroll maps!)

How to Plot a Heatmap?

Plot a Heatmap via Seaborn

Seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt=’.2g’, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels=’auto’, yticklabels=’auto’, mask=None, ax=None, *\kwargs*)

Sorted by importance

  • data: rectangular dataset

    If a Pandas DataFrame is provided, the index/column information will be used to label the columns and rows

  • vmin, vmax: floats, optional

    Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments.

  • linewidths : float, optional

    Width of the lines that will divide each cell.

  • linecolor : color, optional

    Color of the lines that will divide each cell.

  • square : boolean, optional

    If True, set the Axes aspect to “equal” so each cell will be square-shaped.

  • xticklabels, yticklabels : “auto”, bool, list-like, or int, optional

    If True, plot the column names of the dataframe. If False, don’t plot the column names. If list-like, plot these alternate labels as the xticklabels. If an integer, use the column names but plot only every n label. If “auto”, try to densely plot non-overlapping labels.

  • cmap : matplotlib colormap name or object, or list of colors, optional

    The mapping from data values to color space. If not provided, the default will depend on whether center is set.

  • robust : bool, optional

    If True and vmin or vmax are absent, the colormap range is computed with robust quantiles instead of the extreme values.

  • annot : bool or rectangular dataset, optional

    If True, write the data value in each cell. If an array-like with the same shape as data, then use this to annotate the heatmap instead of the raw data.

  • fmt : string, optional

    String formatting code to use when adding annotations.

  • annot_kws : dict of key, value mappings, optional

    Keyword arguments for ax.text when annot is True.

  • cbar : boolean, optional

    Whether to draw a colorbar.

  • cbar_kws : dict of key, value mappings, optional

    Keyword arguments for fig.colorbar.

  • cbar_ax : matplotlib Axes, optional

    Axes in which to draw the colorbar, otherwise take space from the main Axes.

  • mask : boolean array or DataFrame, optional

    If passed, data will not be shown in cells where mask is True. Cells with missing values are automatically masked.

  • ax : matplotlib Axes, optional

    Axes in which to draw the plot, otherwise use the currently-active Axes.

  • kwargs : other keyword arguments

    All other keyword arguments are passed to ax.pcolormesh.

Tips for Designing a Heatmap

  • Use a simple map outline

    These lines are meant to frame the data, not distract.

  • Select colors appropriately

    • Use a single color with varying shade or a spectrum between two analogous colors to show intensity.

    • An important consideration here is selecting colors and contrasts visible to individuals with color blindness.

    • Remember to intuitively code color intensity according to values

  • Use patterns sparingly

    A pattern overlay that indicates a second variable is acceptable, but using multiple is overwhelming and distracting.

  • Choose appropriate data ranges

    Select 3-5 numerical ranges that enable fairly even distribution of data between them. Use +/- signs to extend high and low ranges.

Examples:

https://datavizcatalogue.com/methods/heatmap.html

http://seaborn.pydata.org/generated/seaborn.heatmap.html