Gap in Time Series Plot

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In this article, we’ll explore why there is a gap in your seasonal plot. We’ll start by examining how you’re creating and plotting your data.

Creating Seasonal Data

When working with time series data, it’s common to want to visualize the seasonal patterns in your data. To achieve this, you create separate datasets for each season (winter, spring, summer, fall) and then plot them separately.

Here’s an example of how you might do this:

# Create a sample dataset
import numpy as np
import pandas as pd

np.random.seed(42)

datetime_index = pd.date_range(start='2018-01-01', end='2020-12-31')

volume = np.random.randint(low=30, high=60, size=datetime_index.shape[0])

data = pd.DataFrame({'volume': volume},
                    index=datetime_index)

# Create seasonal data
winter = data['2019-12':'2020-02']
spring = data['2019-03':'2019-05']
summer = data['2019-06':'2019-08']
fall = data['2019-09':'2019-11']

winter.plot()
spring.plot()
summer.plot()
fall.plot()

The Gap in the Winter Plot

Unfortunately, when you run your code, it produces a winter plot with a gap. This can be frustrating because it means that your seasonal patterns don’t look right.

Let’s take a closer look at what might be going on.

Why There is a Gap

In this case, the gap in your plot occurs because you’re displaying the winter months of two different winters: one that started in 2018 and ended in 2019, and another that started in 2019 and ended in 2020.

When you create your seasonal data, you might not be aware of which winter months you’re including. For example:

# Create winter data from the wrong year
winter = data['2018-12':'2019-02']

This means that when you plot winter, it’s actually plotting two separate sets of winter months, one from 2018 and another from 2019.

Similarly, if you’re using a different approach to create your seasonal data, such as:

# Create winter data by concatenating different time ranges
winter = pd.concat([data['2018-12':'2018-12-25'], data['2019-01':'2019-02']])

This will also produce an inconsistent plot with a gap.

Subsetting Your Data for Consistency

To fix the issue, you need to make sure that your seasonal data is consistent across all plots. Here’s how:

# Create seasonal data using date ranges instead of string values
winter = data[(data.index >= '2019-12') & (data.index < '2020-02')]

Or:

# Create winter data from the correct time range
winter = pd.concat([data['2018-12':'2018-12-25'], data['2019-01':'2019-02']])

Filling Gaps in Non-Numeric Data

If you’re working with non-numeric data and can’t create consistent seasonal plots, there are some alternative approaches to filling the gaps:

Light Grey Lines

One approach is to fill the gap with light grey lines. Here’s an example of how to do this:

# Create a plot with light grey lines for missing values
import matplotlib.pyplot as plt

data.plot()
plt.axhline(y=data['2019-02'], color='grey', linestyle='--')
plt.show()

This will produce a plot with a line connecting the data points, but it won’t fill in the gap correctly.

Synthetic Data

Another approach is to create synthetic data for the missing values. Here’s an example of how to do this:

# Create synthetic data for the missing values
import numpy as np

data['2019-02'] = np.random.randint(30, 60)

This will produce a plot with synthetic data that fills in the gap.

Smoothing Time Series Data

Finally, you can also smooth your time series data to improve readability. Here’s an example of how to do this:

# Smooth your time series data using rolling averages
import pandas as pd

data['rolling_avg'] = data['volume'].rolling(3).mean()

This will produce a plot with smoothed time series data that fills in the gap.

Conclusion

In conclusion, when you’re working with seasonal plots and notice gaps in your data, there are several steps to take:

Check that your seasonal data is consistent across all plots.
Use date ranges instead of string values for creating seasonal data.
Fill missing values with light grey lines or synthetic data.

By following these steps, you can create high-quality seasonal plots with minimal gaps in the data.

Last modified on 2024-07-27