Data Visualization
Types of charts
Temporal heat map

Temporal heat map

Definition

Graphic representing a univariate time series time series on a rectangular grid, with color representing values and position on the grid indicating the corresponding time

Description

Temporal heat maps display time series on a rectangular grid, with color representing values and position on the grid indicating the corresponding time, whereby one axis represents a period of time of fixed duration (day, week, year) and the second axis indicates the time point within this period of time.

Use

Consider a temporal heat map if:

  • a given (daily, weekly, etc.) periodicity or seasonality is expected.
  • the data appears too complex for a line plot, for instance because it exhibits a variety of temporal patterns occurring at different frequencies.
  • you want to visualize discrete or even categorical data, which do not render well on line plots, but are a good fit for temporal heat maps.

Strengths

  • Information density. Temporal heat maps offer an information density unmatched in other visualization types, using virtually every pixel. As opposed to line plots, which can be both cluttered and empty in some areas (actually, in most cases, most of the area occupied by a line plot is empty), heat maps unfold all data points on a full rectangle without any lost space.

  • Highlighting of periodic patterns. Daily heat maps, for instance, are the best way to visualize daily patterns, along with seasonal variations and other changes over time. The human eye and brain is adept at discerning patterns in images, whether it be horizontal or vertical lines and bars, color gradients etc., all of which have an interpretation in a heat map.

  • Identification of events, their frequency and duration. While showing all the data, a daytime heat map also allows the reader to identify the date and time of particular events with good precision. Even if the data does not exhibit a daily period, a daily heat map can be useful in showing, for instance, short events (from a few minutes to a few hours), their duration and their frequency (how many a day), as well as how all this evolves over time.

Caveats

  • Temporal heat maps are not as intuitive as line plots, which means they might need a word of introduction for some recipients.

  • Because they use color coding, heat maps may not be the best choice when exact quantities or ratios of the displayed quantity are of primary importance.

Implementation

Daytime heat map using Matplotlib 

"""Temporal heatmaps."""

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


def daytime_heatmap(
    time_series,
    freq=pd.Timedelta("1H"),
    im_kwargs=None,
    n_days_per_x_tick=60,
    n_hrs_per_y_tick=3,
    date_format="%Y-%m-%d",
    ax=None,
    cbar_title="",
):
    """Plot a daytime heat map of a time series

    Args:
        time_series (pd.Series): a pandas time series
        freq (pd.Timedelta): frequency with which to resample the time series
        im_kwargs (dict): kwargs to pass to plt.imshow
        n_days_per_x_tick (int): number of days per x tick
        n_hrs_per_y_tick (int): number of hours per y tick
        date_format (str): format of x axis ticks
        ax (plt.Axes): Matplotlib axes to plot on
        cmap (str): name of colormap to use
        cbar_title (str): title of colorbar
    """

    if im_kwargs is None:
        im_kwargs = {}

    # resampling and pivoting
    time_series = time_series.resample(freq).mean()
    long_df = pd.DataFrame(time_series)
    long_df.columns = ["value"]
    long_df["date"] = long_df.index.normalize()
    long_df["daytime"] = long_df.index - long_df["date"]

    short_df = long_df.pivot_table(columns="date", index="daytime", values="value")

    if ax is None:
        _, ax = plt.subplots(figsize=(10, 5))

    # the actual heatmap plotting
    img = ax.imshow(short_df, interpolation="none", **im_kwargs)
    plt.colorbar(img, label=cbar_title)

    # formatting y axis ticks (time of day)
    daytimes = sorted(list(set(long_df["daytime"])))
    n_per_y_tick = int(pd.Timedelta(f"{n_hrs_per_y_tick}H") / freq)
    y_ticks = np.arange(0, len(daytimes), n_per_y_tick)

    def daytime_string(time_delta):
        return f"{time_delta.components.hours:02d}:{time_delta.components.minutes:02d}"

    y_tick_labels = [daytime_string(daytimes[i_tick]) for i_tick in y_ticks]
    ax.set_yticks(y_ticks - 0.5, labels=y_tick_labels)

    # formatting y axis ticks (time of day)
    dates = sorted(list(set(long_df["date"])))
    x_ticks = np.arange(0, len(dates), n_days_per_x_tick)
    x_tick_labels = [dates[i_tick].strftime(date_format) for i_tick in x_ticks]
    ax.set_xticks(x_ticks, labels=x_tick_labels)
    ax.set_xlabel("Date")
    ax.set_ylabel("Time of day")
    ax.set_title("Daytime heat map")

See also

StreamgraphTree map