Imputing missing values
When you have missing data points, how can you fill them in?
In this exercise, you'll practice using different interpolation methods to fill in some missing values,
visualizing the result each time. But first, you will create the function (interpolate_and_plot()) you'll use to interpolate missing data points and plot them.
A single time series has been loaded into a DataFrame called prices.
Cet exercice fait partie du cours
Machine Learning for Time Series Data in Python
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Create a function we'll use to interpolate and plot
def interpolate_and_plot(prices, interpolation):
# Create a boolean mask for missing values
missing_values = prices.____()
# Interpolate the missing values
prices_interp = prices.____(interpolation)
# Plot the results, highlighting the interpolated values in black
fig, ax = plt.subplots(figsize=(10, 5))
prices_interp.plot(color='k', alpha=.6, ax=ax, legend=False)
# Now plot the interpolated values on top in red
prices_interp[missing_values].plot(ax=ax, color='r', lw=3, legend=False)
plt.show()