Single Plot Charts with Chartly Examples¶

Line Plot¶

A line plot can be created by using the line_plot keyword. Users can pass both x and y data or just y data to the line plot.

The following customization options are available for the Line Plot:

color (str): The color of the line.
linestyle (str): The style of the line.

import chartly
import numpy as np

# Only y data
data = np.random.randn(300)

# customize the plot
customs = {"color": "red", "linestyle": "--"}

# label the plot
super_axes_labels = {"super_ylabel": "Y", "super_title": "Generic Plot Example"}

# create a chart instance
plot = chartly.Chart(super_axes_labels)

# Set the arguments for the plot
plot_payload = {
    "plot": "line_plot",
    "data": data,
    "customs": customs,
}

# Create a subplot and plot the data
plot.new_subplot(plot_payload)

# Build and display the main figure
plot()

import chartly
import numpy as np

# x and y data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# define plot labels
super_axes_labels = {
    "super_ylabel": "Y",
    "super_xlabel": "X",
    "super_title": "Generic Plot Example with X & Y",
}

# create a chart instance
plot = chartly.Chart(super_axes_labels)

# customize the plot
customs = {"color": "mediumpurple", "linestyle": "--"}

# set the arguments
plot_payload = {"plot": "line_plot", "data": [x, y], "customs": customs}

# Create a subplot and plot the data
plot.new_subplot(plot_payload)

# Build and display the main figure
plot()

Histogram¶

A histogram plot can be created using the histogram keyword. The histogram plot requires a 1D array of data to be passed to the plot. The following customization options are available for the histogram plot:

color (str): The color of the histogram.
num_bins (int): The number of bins in the histogram.
ran (tuple): The range of the histogram.

import chartly
import numpy as np

data = np.random.randn(1000)

# label the plot
super_axes_labels = {
    "super_ylabel": "Frequency",
    "super_xlabel": "Value",
    "super_title": "Histogram Plot Example",
}

plot = chartly.Chart(super_axes_labels)

# customize the plot
customs = {"color": "salmon", "num_bins": 30}

# set the arguments
plot_payload = {"plot": "histogram", "data": data, "customs": customs}

# Create a subplot and plot the data
plot.new_subplot(plot_payload)

# Build and display the main figure
plot()

Contour Plot¶

Contour plots can be created using the contour keyword. The contour plot requires 2D arrays of X, Y and Z data to be passed to the plot. The following customization options are available for the contour plot:

inline? (bool): Whether the contour labels are inline or not.
color (str): The color of the contour plot. Ensure that the cmap is set to None.
levels (int): The number of contour levels.
colormap (str): The colormap of the contour plot.
filled? (bool): Whether the contour plot is filled or not.
fontsize (int): The font size of the contour labels.
hatch? (bool): Whether the contour plot has a hatched area or not.
hatch_customs (dict): The hatch customization options.

 import chartly
 import numpy as np

 x = np.linspace(-3.0, 3.0, 100)
 y = np.linspace(-3.0, 3.0, 100)
 X, Y = np.meshgrid(x, y)
 Z = np.cos(X/3) * np.sin(Y/3)

 # label the plot
 super_axes_labels = {
     "super_xlabel": "X",
     "super_ylabel": "Y",
     "super_title": "Contour Plot Example",
}

 # Create a charts instance
 plot = chartly.Chart(super_axes_labels)

 # customize the plot
 customs = {"colormap": "magma", "fontsize": 14, "filled?": True}

 # set the arguments
 plot_payload = {"plot": "contour", "data": [X, Y, Z], "customs": customs}

 # Create a subplot and plot the data
 plot.new_subplot(plot_payload)

 # Build and display the main figure
 plot()

Normal Probability Plot¶

The normal probability plot is used to determine if a dataset is approximately normally distributed. A normal probability plot can be created using the probability_plot keyword. The normal probability plot requires a 1D array of data to be passed to the plot. The following customization options are available for the normal probability plot:

color (str): The color of the markers of the plot.

import chartly
import numpy as np

data = np.random.randn(150)

# label the plot
super_axes_labels = {"super_title": "Normal Probability Plot Example"}

# create a chart instance
plot = chartly.Chart(super_axes_labels)

# customize the plot
customs = {"color": "firebrick"}

# set the arguments
args = {"plot": "probability_plot", "data": data, "customs": customs}

# Create a subplot and plot the data
plot.new_subplot(args)

# build and display the main figure
plot()

Cumulative Distribution Function Plot¶

The CDF plot of a dataset can be created using the cdf keyword. The CDF plot requires a 1D array of data to be passed to the plot. The following customization options are available for the CDF plot:

color (str): The color of the CDF plot.

import chartly
import numpy as np

data = np.random.exponential(scale=1.0, size=500)

# label the main figure
super_axes_labels = {
    "super_title": "Cumulative Distribution Function Plot Example",
    "super_ylabel": "Probability",
}

# Create a chart instance
plot = chartly.Chart(super_axes_labels)

# customize the plot
axes_labels = {"linelabel": "CDF"}
args = {"plot":"cdf", "data": data, "axes_labels": axes_labels}

# Create a subplot and plot the data
plot.new_subplot(args)

# Build and display the main figure
plot()

Normal Cumulative Distribution Function Plot¶

The CDF of a dataset can be compared to the CDF of a normal distribution using the normal CDF plot. The normal CDF plot can be created using the normal_cdf keyword. Users can pass multiple datasets to the plot.

import chartly
import numpy as np

dataset_one = np.random.exponential(scale=1.0, size=500)
dataset_two = np.random.normal(loc=2, scale=1, size=500)
dataset_three = np.random.gamma(2, 2, 500)
data = [dataset_one, dataset_two, dataset_three]

# label the main figure
super_axes_labels = {"super_title": "Normal Cumulative Distribution Function Plot Example"}

# create a chart instance
plot = chartly.Chart(super_axes_labels)

# set the arguments
args = {"plot": "normal_cdf", "data": data}

# Create a subplot and plot the data
plot.new_subplot(args)

# Build and display the main figure
plot()

Density Plot¶

The density function of a distribution can be created using the density keyword. The density plot requires a 1D array of data to be passed to the plot. The following customization options are available for the density plot:

color (str): The color of the density plot.
fill (bool): Whether the density plot is filled or not.

import chartly
import numpy as np

data = np.random.exponential(scale=1.0, size=500)

# label the plot
super_axes_labels = {"super_title": "Density Plot Example"}

# create a chart instance
plot = chartly.Chart(super_axes_labels)

# define the customs
customs = {"fill": True, "color": "mediumvioletred", "label": "density"}

# set the arguments
plot_payload = {"plot": "density", "data": data, "customs": customs}

# Create the subplot and plot density plot
plot.new_subplot(plot_payload)

# Build and display the main figure
plot()

Box Plot¶

A boxplot of one or more datasets can be plotted using the BoxPlot class. A dataset list or a list of dataset lists can be passed to the BoxPlot plot. The following customization options are available for the BoxPlot plot:

showfliers (bool): Whether to show the outliers in the boxplot.
boxlabels (list): The labels of the boxplots.

import chartly
import numpy as np

dataset_one = np.random.exponential(scale=1.0, size=500)
dataset_two = np.random.normal(loc=2, scale=1, size=500)
dataset_three = np.random.gamma(2, 2, 500)
data = [dataset_one, dataset_two, dataset_three]

# label the main figure
super_axes_labels = {"super_title": "BoxPlot Example"}

# create a chart instance
plot = chartly.Chart(super_axes_labels)

# define the customs
customs = {"showfliers": False}

# set the arguments
plot_payload = {"plot": "boxplot", "data": data, "customs": customs}

# Create new subplot and plot the boxplot
plot.new_subplot(plot_payload)

# Build and display the main figure
plot()

Single Plot Charts with Chartly Examples¶

Line Plot¶

Histogram¶

Contour Plot¶

Normal Probability Plot¶

Cumulative Distribution Function Plot¶

Normal Cumulative Distribution Function Plot¶

Density Plot¶

Box Plot¶

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