Histogram Plot

A histogram plot is a data visualization technique commonly used in statistics and data analysis to visualize the distribution of a single continuous variable. It consists of a series of contiguous, non-overlapping bars that provide a visual summary of the frequency or density of data points within predefined intervals or “bins.” The number of bins significantly impacts the visualization.

Histograms are appropriate when the data contain a continuous variable of interest. If there is an additional categorical variable that the variable of interest depends on, layered histograms may be appropriate using the by argument.

What are histograms useful for?

• Data distribution analysis: Histograms are a valuable tool to gain insights into the distribution of a dataset, making it easier to understand the central tendencies, spread, and skewness of the data.
• Identifying outliers: Histograms help in detecting outliers or anomalies in a dataset by highlighting data points that fall outside the typical distribution.
• Density estimation: Histograms can serve as the basis for density estimation methods, helping to model and understand underlying data distributions, which is crucial in statistical analysis and machine learning.

Examples

A basic histogram

Visualize the distribution of a single variable by passing the column name to the x or y arguments.

import deephaven.plot.express as dx
iris = dx.data.iris()

# subset to get specific species
setosa = iris.where("Species == `setosa`")

# control the plot orientation using `x` or `y`
hist_plot_x = dx.histogram(setosa, x="SepalLength")
hist_plot_y = dx.histogram(setosa, y="SepalLength")

Modify the bin size by setting nbins equal to the number of desired bins.

import deephaven.plot.express as dx
iris = dx.data.iris()

# subset to get specific species
virginica = iris.where("Species == `virginica`")

# too many bins will produce jagged, disconnected histograms
hist_20_bins = dx.histogram(setosa, x="SepalLength", nbins=20)

# too few bins will mask distributional information
hist_3_bins = dx.histogram(setosa, x="SepalLength", nbins=3)

# play with the `nbins` parameter to get a good visualization
hist_8_bins = dx.histogram(setosa, x="SepalLength", nbins=8)

Bin and aggregate on different columns

If the plot orientation is vertical ("v"), the x column is binned and the y column is aggregated. The operations are flipped if the plot orientation is horizontal.

import deephaven.plot.express as dx
iris = dx.data.iris()

# subset to get specific species
setosa = iris.where("Species == `setosa`")

# The default orientation is "v" (vertical) and the default aggregation function is "sum"
hist_v = dx.histogram(setosa, x="SepalLength", y="SepalWidth")

# Control the plot orientation using orientation
hist_h = dx.histogram(setosa, x="SepalLength", y="SepalWidth", orientation="h")

# Control the aggregation function using histfunc
hist_avg = dx.histogram(setosa, x="SepalLength", y="SepalWidth", histfunc="avg")

Distributions of several groups

Histograms can also be used to compare the distributional properties of different groups of data, though they may be a little harder to read than box plots or violin plots. Pass the name of the grouping column(s) to the by argument.

import deephaven.plot.express as dx
iris = dx.data.iris()

# each bin may be stacked side-by-side for each group
stacked_hist = dx.histogram(iris, x="SepalLength", by="Species")

# or, each bin may be overlaid with the others
overlay_hist = dx.histogram(iris, x="SepalLength", by="Species", barmode="overlay")

API Reference

Returns a histogram

Returns: DeephavenFigure A DeephavenFigure that contains the histogram