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.
Diese Übung ist Teil des Kurses
Machine Learning for Time Series Data in Python
Anleitung zur Übung
- 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?
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# 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)