Xtick Frequency in Pandas Boxplot

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In this article, we will explore the issue of xtick frequency in pandas boxplots and provide a solution to achieve a more readable plot.

Introduction

When working with large datasets, it’s common to encounter issues with data visualization, particularly when dealing with categorical variables. In this case, we’re using pandas groupby to create a bar and whisker plot of wind speed vs direction. However, the x-axis becomes cluttered due to many values close together.

Problem Statement

The problem statement is as follows:

“I am using pandas groupby for plotting wind speed Vs direction using a bar and whisker plot. However, the x-axis is not readable due to so many wind direction value close to each other.”

Solution

To address this issue, we need to reduce the frequency of the xtick values. This can be achieved by selecting a subset of unique values from the categorical variable and using them as tick labels.

One approach is to use the groupby function with a specific condition to select the desired subset of data.

Example Code

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample dataframe
df = pd.DataFrame({
    'Kvit_TIU': [0.064740, 0.057442, 0.056750, 0.069002, 0.068464,
                 0.067057, 0.071901, 0.050464, 0.066165, 0.073993,
                 0.090784, 0.121366, 0.087172, 0.066197, 0.073020,
                 0.071784, 0.081699, 0.088014, 0.076758, 0.078574],
    'dir_cat': [14, 15, 15, 17, 17, 17, 12, 5, 1, 27, 34, 33,
                34, 30, 17, 16, 17, 14, 14, 14]
})

# Group by 'dir_cat' and plot the boxplot
fig = plt.figure()
ax1 = df.boxplot(column='Kvit_TIU', by='dir_cat', showfliers=False, showmeans=True)

# Select only the unique values for 'dir_cat'
unique_dirs = df['dir_cat'].unique()

# Set the xtick labels to every 10th value
xtick_labels = unique_dirs[::10]
ax1.set_xticks([i * 360 / len(unique_dirs) for i in range(len(xtick_labels))])

plt.show()

Explanation

In this example, we first create a sample dataframe with the required columns. We then use the boxplot function to create the bar and whisker plot of wind speed vs direction.

Next, we select only the unique values for the ‘dir_cat’ column using the unique() method. Finally, we set the xtick labels to every 10th value using list comprehension.

By doing so, we reduce the frequency of the xtick values, making it easier to read and interpret the plot.

Alternative Solution

Another approach is to use the groupby function with a specific condition to select the desired subset of data. For example:

import pandas as pd
import matplotlib.pyplot as plt

# Create a sample dataframe
df = pd.DataFrame({
    'Kvit_TIU': [0.064740, 0.057442, 0.056750, 0.069002, 0.068464,
                 0.067057, 0.071901, 0.050464, 0.066165, 0.073993,
                 0.090784, 0.121366, 0.087172, 0.066197, 0.073020,
                 0.071784, 0.081699, 0.088014, 0.076758, 0.078574],
    'dir_cat': [14, 15, 15, 17, 17, 17, 12, 5, 1, 27, 34, 33,
                34, 30, 17, 16, 17, 14, 14, 14]
})

# Group by 'dir_cat' and plot the boxplot
fig = plt.figure()
ax1 = df.dropna().boxplot(column='Kvit_TIU', by='dir_cat', showfliers=False, showmeans=True)

# Select only the values for 'dir_cat' >= 30
df_filtered = df[df['dir_cat'] >= 30]
xtick_labels = df_filtered['dir_cat'].unique()
ax1.set_xticks([i * 360 / len(xtick_labels) for i in range(len(xtick_labels))])

plt.show()

In this alternative solution, we first select only the values for ‘dir_cat’ >= 30 using boolean indexing. We then use the boxplot function to create the bar and whisker plot of wind speed vs direction.

Finally, we set the xtick labels to every 10th value using list comprehension.

By doing so, we also reduce the frequency of the xtick values, making it easier to read and interpret the plot.

Conclusion

In conclusion, reducing the frequency of the xtick values is an effective way to improve the readability of a boxplot. By selecting a subset of unique values from the categorical variable and using them as tick labels, we can make it easier to compare and analyze the data.

This technique can be applied to various types of plots, including scatterplots, histograms, and violin plots. By following these steps, you can ensure that your plots are easy to read and interpret, even for large datasets.

Last modified on 2024-08-31