Visualize the seasonality of multiple time series

You will now extract the seasonality component of jobs_decomp to visualize the seasonality in these time series. Note that before plotting, you will have to convert the dictionary of seasonality components into a DataFrame using the pd.DataFrame.from_dict() function.

An empty dictionary jobs_seasonal and the time series decomposition object jobs_decomp from the previous exercise are available in your workspace.

Diese Übung ist Teil des Kurses

Visualizing Time Series Data in Python

Kurs anzeigen

Anleitung zur Übung

Iterate through each column name in jobs_names and extract the corresponding seasonal component from jobs_decomp. Place the results in the jobs_seasonal, where the column name is the name of the time series, and the value is the seasonal component of the time series.
Convert jobs_seasonal to a DataFrame and call it seasonality_df.
Create a facetted plot of all 16 columns in seasonality_df. Ensure that the subgraphs do not share y-axis.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Extract the seasonal values for the decomposition of each time series
for ts in ____:
    jobs_seasonal[ts] = jobs_decomp[ts]____
    
# Create a DataFrame from the jobs_seasonal dictionary
____ = ____(jobs_seasonal)

# Remove the label for the index
seasonality_df.index.name = None

# Create a faceted plot of the seasonality_df DataFrame
____(subplots=____,
                   layout=____,
                   sharey=____,
                   fontsize=2,
                   linewidth=0.3,
                   legend=False)

# Show plot
plt.show()

Code bearbeiten und ausführen