Understanding the Issue with Plotting Line Data

Introduction

In this article, we will explore a common issue in data visualization that arises when plotting line charts. The problem is that not all values in the x-axis are displayed, leading to an incomplete representation of the data. We will delve into the technical aspects of this issue and provide solutions to ensure accurate plotting.

Background Information

When creating plots using popular libraries like matplotlib or seaborn in Python, we often use functions that group data by a specific column (in this case, ‘DAY_DEPOSE’). These functions can create line charts, scatter plots, or bar charts, depending on the type of plot selected. The x-axis represents the unique values from the specified column.

The Problem with plot() Function

In our example code snippet, we use the plot() function to create a line chart:

out2.plot(kind='line', figsize=(20,18))

This function groups the data by ‘DAY_DEPOSE’ and then plots it as a line chart. However, the issue arises when the plot() function skips or doesn’t show some values from the x-axis.

Why Does This Happen?

There are several reasons why this might occur:

1. Missing Values: If there are missing values (NaNs) in the data, they may not be displayed on the plot.
2. Duplicate Values: If multiple rows have the same value for ‘DAY_DEPOSE’, it can lead to confusion and skipping of some values during plotting.
3. Data Type Issues: The x-axis type (e.g., categorical or numerical) might cause some values to be skipped.

How to Fix This Issue

To resolve this issue, let’s explore a few possible solutions:

Solution 1: Handling Missing Values

If there are missing values in the data, we can use the dropna() function from pandas to remove them before plotting:

out2 = out2.dropna()

This ensures that all values are displayed on the plot.

Solution 2: Removing Duplicate Values

Duplicate values can also cause issues with plotting. We can use the drop_duplicates() function to remove duplicate rows:

out2 = out2.drop_duplicates(subset='DAY_DEPOSE')

However, this may lead to loss of data if there are multiple occurrences of the same value.

Solution 3: Changing Data Type

The x-axis type might be causing some values to be skipped. If ‘DAY_DEPOSE’ is a categorical variable, we can use plt.xticks() with the rotation parameter to rotate labels and display all values:

ax = out2.plot(kind='line', figsize=(20,18))
plt.xticks(rotation=45)

Alternatively, if ‘DAY_DEPOSE’ is numerical, we can ensure that it’s correctly aligned with the x-axis.

Solution 4: Specifying Custom X-Axis

We can also specify a custom x-axis using plt.gca().set_xticks():

ax = out2.plot(kind='line', figsize=(20,18))
xtick_values = [1, 5, 11, 16, 21, 26]  # custom x-axis values
plt.xticks(xtick_values)

This allows us to manually specify the x-axis values and their positions.

Additional Considerations

When plotting line charts, it’s essential to consider other aspects of data visualization, such as:

• Data normalization: Normalizing the y-axis can help improve plot readability.
• Plot title and labels: Adding a clear plot title and axis labels enhances understanding.
• Legend placement: Positioning the legend is crucial for intuitive interpretation.

Conclusion

Plotting line charts can sometimes result in skipped values on the x-axis. By understanding the reasons behind this issue and applying the solutions outlined above, we can ensure accurate and informative visualization of our data. Remember to consider additional aspects of data visualization to create a clear and effective plot.

Last modified on 2025-05-02