How to make Stripplot with Jitter in Altair Python?

This tutorial will explain how to make a Stripplot using Jitter in Altair Python. Use bar charts with dithering in Altair Python to quickly and easily visualize data sets containing continuous and categorical variables. In a strip chart, one of the variables is a categorical variable and the other is a continuous variable. A strip chart is a type of scatter chart. By viewing the data points as individual points along the categorical axis, we can see the distribution of the continuous variable for each category. Dithering the data points across the plot makes it easier to examine how the data is distributed.

Using the mark_circle() function to build a chart and the jitter() function to add a jitter effect, we can create a bar chart with a jitter effect in Altair Python. First, the x and y variables of the chart must be specified using the encoding parameter. Then use the mark_circle() function to create the chart and the jitter() function to add the jitter effect. You can also modify the chart by modifying the axis labels, color scheme, and adding a title. By following these simple methods, we can create a readable and instructive bar chart with jitter effect in Altair Python.

grammar

Altair is a Python library that can be used to create strip charts with dithering. The following is a syntax example for creating a strip chart with dithering using Altair -

import altair as alt

# create a stripplot with jitter using Altair
alt.Chart(df).mark_circle(size = 14).encode(
   x = alt.X('jitter:Q', title = None, axis = alt.Axis(ticks = True, grid = False, labels = False), scale = alt.Scale()),
   y = alt.Y('Y:Q', scale = alt.Scale()),
   color = alt.Color('C:N', legend = None),
).transform_calculate(
   Jitter = 'sqrt(-2*log(rand()))*cos(2*PI*rand())',
)

The given code creates a bar chart with jitter effect using Altair. The transform_calculate() method calculates the square root of the negative twice the natural logarithm of a random number generated by the rand() method in Python's random module, multiplied by twice pi times another random number generated using the same rand() method. The cosine value of , generates a Gaussian jitter on the x-axis. This adds the jitter value to the 'jitter' column of the DataFrame. This jitter value is then mapped onto the x-axis using the 'jitter:Q' encoding in the alt.X() method.

Example

The following code uses the Altair visualization library in Python to generate a bar chart with a jitter effect. The code first uses pandas.DataFrame() to create a custom dataset containing 100 randomly generated x values, y values, and categories. The x and y values ​​represent the coordinates of the points in the plot, while the category column determines the color of each point.

Then use the alt.Chart() function to create a chart object, and call the mark_circle() function to specify that the plot should be circular. The encode() method is used to specify how data is mapped to the visual properties of the plot, such as x and y positions and point colors. In this case, x encoding adds jitter to the x-axis using a calculated field called jitter, while y encoding specifies the y value. Color coding uses the category column to color the points, and the legend=None parameter removes the legend. Finally, the transform_calculate() function is used to calculate the jitter field using a formula based on a random number generator that adds a small amount of random noise to the x-values ​​and spreads the points horizontally.

import altair as alt
import pandas as pd
import numpy as np

# create a custom dataset
custom_data = pd.DataFrame({
   'x_values': np.random.randn(100),
   'y_values': np.random.randn(100),
   'category': np.random.choice(['A', 'B', 'C'], 100)
})

# create a stripplot with jitter using Altair
alt.Chart(custom_data).mark_circle(size = 14).encode(
   x=alt.X('jitter:Q', title = None, 
   axis = alt.Axis(ticks = True, grid = False, labels = False), scale = alt.Scale()),
   y=alt.Y('y_values:Q', scale=alt.Scale()),
   color=alt.Color('category:N', legend = None),
).transform_calculate(
   jitter='sqrt(-2*log(random()))*cos(2*PI*random())',
)

Output

Example

This example shows how to create a strip chart with jitter in Altair using the Iris dataset. The code first imports the necessary libraries from the vega_datasets library, including the Altair and Iris datasets. It then creates an Altair chart using the mark_circle method, creating a circle for each data point and encoding the x, y and color variables using the Altair X, Y and Color classes respectively.

This example shows how to create a strip chart with jitter in Altair using the Iris dataset. The code first imports the necessary libraries from the vega_datasets library, including the Altair and Iris datasets. It then creates an Altair chart using the mark_circle method, creating a circle for each data point and encoding the x, y and color variables using the Altair X, Y and Color classes respectively.

import altair as alt
from vega_datasets import data

# load the Iris dataset
iris = data.iris()

# create a stripplot with jitter using Altair
alt.Chart(iris).mark_circle(size = 14).encode(
   x = alt.X('jitter:Q', title = None, axis = alt.Axis(ticks = True, grid = False, labels = False), scale = alt.Scale()),
   y = alt.Y('petalWidth:Q', scale = alt.Scale()),
    color = alt.Color('species:N', legend = None),
).transform_calculate(
   jitter = 'sqrt(-2*log(random()))*cos(2*PI*random())',
)

Output

Conclusion

In summary, using dithering to create bar charts is very useful for showing the distribution and variability of data points. Python's Altair package makes doing this easy and efficient. Users can follow the instructions provided in this article, including importing the required libraries, loading data, and encoding x, y, and color variables, to create an educational and beautiful chart. By using the transform_calculate method to include jitter, the chart is further improved as it is now easier to identify specific data points and patterns in the data.

Overall, Altair is a powerful Python data visualization tool, and using dither to create a strip chart is just one example of its capabilities. You can create a variety of powerful and educational visualizations by experimenting with various data sets and visual encodings. Thanks to Altair's simple syntax and powerful functionality, the data visualization possibilities are endless.

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