How to Plot Selected Rows with Datetime on X-axis in Python

Learn how to effectively plot a selected time range of currency exchange data using Python's matplotlib, with datetime on the x-axis and close values on the y-axis.
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How to Plot Selected Rows with Datetime on X-axis in Python

When analyzing time series data, such as currency exchange rates, one often needs to visualize specific intervals of that data. However, plotting the data can sometimes lead to confusion, especially with datetime information on the x-axis. If you're working with a dataset that features datetime entries and wish to plot them against another variable across a limited timeframe, you've come to the right place!

In this post, we'll explore how to select specific rows from your dataset based on datetime values and plot them, allowing for clear insights over that chosen period.

Understanding the Problem

Let's say you have currency exchange data in 15-minute intervals, spanning three days. You have already converted your date column into datetime format, which is excellent for plotting. However, when plotting with matplotlib, you might notice that the x-axis displays only a limited range of values, which can include row numbers instead of datetime values. Here’s an example of what you might encounter in your plotting:

[[See Video to Reveal this Text or Code Snippet]]

This could lead to issues if you want to focus your plot on specific time ranges, such as from 9 a.m. to 1 p.m. on a given day. Your challenge is determining how to extract that specific window of time for the plot.

Solution Steps

To effectively tackle this, we will need to slice our data based on datetime conditions. Below are the steps to achieve the desired result.

Step 1: Define Time Ranges

First, we need to define the time range we're interested in. In your case, that means selecting data from 9 a.m. to 1 p.m.

Step 2: Create an Index for Selections

Next, we will create a boolean index that marks entries within the desired time range. This index will help filter the dataset.

Step 3: Plot the Filtered Data

Finally, we can use this index to plot only the selected rows using matplotlib.

Example Code

Now, let's implement the steps outlined above in Python code:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the Code:

Creating Conditions: The line idx = (EURJPY['Date'] > '07/05/2021 09:00') & (EURJPY['Date'] <= '07/05/2021 13:00') establishes the time frame for our plot.

Filtering Dataset: EURJPY.loc[idx, 'Date'] and EURJPY.loc[idx, 'Close'] ensure that we are only taking the selected rows defined by our earlier condition.

Plotting: Finally, the plotting commands display the filtered data, allowing us to visualize trends during that specified time range.

Conclusion

With just a few lines of code and a clear understanding of how to filter your data, you can visualize specific intervals of your currency exchange data effectively. This method not only enhances your analysis but also provides clearer insights into fluctuations during defined periods.

If you have any questions or further challenges while plotting your data, feel free to ask. Happy plotting!