Visualizing messy data
Let's take a look at a new dataset - this one is a bit less-clean than what you've seen before.
As always, you'll first start by visualizing the raw data. Take a close look and try to find datapoints that could be problematic for fitting models.
The data has been loaded into a DataFrame called prices.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Exercise instructions
- Visualize the time series data using Pandas.
- Calculate the number of missing values in each time series. Note any irregularities that you can see. What do you think they are?
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Visualize the dataset
prices.____(legend=False)
plt.tight_layout()
plt.show()
# Count the missing values of each time series
missing_values = prices.____.____
print(missing_values)