Fixing Unexpected Plotting Errors in Altair for Python Visualizations

Troubleshooting Plot Display Issues in Altair

Altair is a popular declarative visualization library in Python, especially known for its concise and elegant code. However, even with the simplest datasets, errors can occur, leading to unexpected display issues. One such problem involves plotting geospatial data using random latitude and longitude values.

In this article, we will explore a specific issue encountered while plotting a map-like visualization in Altair. Although the code seems correct, the output in VSCode produces a strange error that is difficult to troubleshoot. The aim is to understand why this happens and how to resolve it.

The dataset being used includes latitude and longitude coordinates, alongside additional information such as the month and voucher counts. While the data appears to be well-structured, the chart renders incorrectly, despite using seemingly appropriate parameters. This creates a roadblock for users trying to visualize the data properly.

Through a detailed analysis of the code and the data types involved, we will identify the root cause of the error and provide step-by-step guidance on how to fix this Altair plotting issue. Whether you're new to data visualization or an experienced user, this guide will help you avoid common pitfalls.

Understanding and Troubleshooting Altair Plotting Errors

In the example above, we are using Altair to plot geospatial data points on a map-like visualization, using randomly generated latitude and longitude values. The primary purpose of this visualization is to show vouchers distributed over different months, using various parameters like the size of the markers to represent the number of vouchers. One of the key challenges faced when plotting such data is ensuring that overlapping points (for close latitudes and longitudes) do not clutter the chart, which is why jittering is introduced.

The script begins by generating random latitude and longitude data using numpy’s random number functions. These functions simulate geographic data, and in conjunction with pandas, this data is organized into a DataFrame for easy handling. By using mark_circle() in Altair, each data point is visually represented as a circle on the map. The circles are sized using the alt.Size() encoding, which scales them according to the number of vouchers per location, helping the viewer easily interpret the quantity associated with each data point.

One common issue, however, is that data points with very close or identical coordinates can overlap, making the visualization less clear. To solve this, the second approach introduces jittering, where a small random offset is applied to both the latitude and longitude values. This makes each point slightly different and helps to avoid overlap. By adding the jittered values as new fields in the DataFrame, Altair can plot these altered coordinates instead of the original ones, ensuring a more readable visualization without sacrificing the accuracy of the data.

The script also incorporates unit tests using the unittest library to verify the functionality of the plotting code. The test case checks whether the Altair chart is correctly instantiated and if the jittering logic works as expected. This combination of visualization and testing ensures that the solution is not only visually effective but also reliable and maintainable in the long run. Adding tooltips to the chart further enhances usability by providing detailed information about each point on hover, giving users a quick way to inspect the underlying data.

import altair as alt
import pandas as pd
import numpy as np
# Generate random data for plottinglats = np.random.uniform(51.5, 51.6, 100)
lons = np.random.uniform(-0.1, 0.1, 100)
months = np.arange(1, 13)
vouchers = np.random.randint(1, 100, 100)
# Create DataFrametest_df = pd.DataFrame({'lat': lats, 'lon': lons, 'month': np.random.choice(months, 100), 'vouchers': vouchers})
# Plot using Altair with correct encodingchart = alt.Chart(test_df).mark_circle().encode(
    longitude='lon:Q',
    latitude='lat:Q',
    size='vouchers:Q',
    color='month:N',
    tooltip=['lat', 'lon', 'vouchers']
)
chart.show()

import altair as alt
import pandas as pd
import numpy as np
# Adding jitter to avoid overlapping points
test_df['lat_jittered'] = test_df['lat'] + np.random.uniform(-0.001, 0.001, len(test_df))
test_df['lon_jittered'] = test_df['lon'] + np.random.uniform(-0.001, 0.001, len(test_df))
# Plot with jittered coordinateschart_jittered = alt.Chart(test_df).mark_circle().encode(
    longitude='lon_jittered:Q',
    latitude='lat_jittered:Q',
    size=alt.Size('vouchers:Q', scale=alt.Scale(range=[0, 1000]), legend=None),
    color=alt.value('blue'),
    tooltip=['lat_jittered', 'lon_jittered', 'vouchers']
)
chart_jittered.show()

import unittest
import altair as alt
import pandas as pd
import numpy as np
class TestAltairPlots(unittest.TestCase):
    def setUp(self):
        self.test_df = pd.DataFrame({'lat': np.random.uniform(51.5, 51.6, 100),
                                     'lon': np.random.uniform(-0.1, 0.1, 100),
                                     'vouchers': np.random.randint(1, 100, 100)})
    def test_plot_creation(self):
        chart = alt.Chart(self.test_df).mark_circle().encode(
            longitude='lon:Q', latitude='lat:Q', size='vouchers:Q')
        self.assertTrue(chart is not None)

if __name__ == '__main__':
    unittest.main()

Exploring Altair's Flexibility in Data Visualization

One important aspect of working with Altair is its ability to seamlessly handle complex datasets while maintaining a simple and declarative approach to data visualization. Altair uses the Vega-Lite grammar, which allows users to build interactive visualizations by mapping data fields to visual properties like color, size, and shape. This makes Altair a powerful tool for quickly generating insightful visualizations from raw data, especially in cases where geographic plotting or multiple categories are involved.

Another critical feature of Altair is its support for interactivity. By using built-in functions like selections, users can easily filter and highlight data on the chart. This is extremely useful for exploring geospatial data, where selecting a specific region or time frame can provide deeper insights. Interactivity also allows users to drill down into the data by combining selections with transformations, making it possible to add dynamic elements like zoom or pan controls, or custom tooltips.

When dealing with complex visualizations, like the map we discussed, it’s essential to manage potential errors or display issues. Sometimes, these errors come from incorrect data encoding or unsupported data types. Ensuring that the data being plotted is of the correct type (e.g., quantitative for numerical values or nominal for categorical values) is critical for producing accurate visualizations. Properly handling data formats and adding error handling in your scripts can save significant time and effort in debugging.