Correlations between multiple time series
In the previous exercise, you extracted the seasonal component of each time series in the jobs DataFrame and stored those results in new DataFrame called seasonality_df. In the context of jobs data, it can be interesting to compare seasonality behavior, as this may help uncover which job industries are the most similar or the most different.
This can be achieved by using the seasonality_df DataFrame and computing the correlation between each time series in the dataset. In this exercise, you will leverage what you have learned in Chapter 4 to compute and create a clustermap visualization of the correlations between time series in the seasonality_df DataFrame.
Diese Übung ist Teil des Kurses
Visualizing Time Series Data in Python
Anleitung zur Übung
- Compute the correlation between all columns in the
seasonality_dfDataFrame using the spearman method and assign the results toseasonality_corr. - Create a new clustermap of your correlation matrix.
- Print the correlation value between the seasonalities of the Government and Education & Health industries.
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Get correlation matrix of the seasonality_df DataFrame
seasonality_corr = ____
# Customize the clustermap of the seasonality_corr correlation matrix
fig = ____(____, annot=True, annot_kws={"size": 4}, linewidths=.4, figsize=(15, 10))
plt.setp(fig.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
plt.setp(fig.ax_heatmap.xaxis.get_majorticklabels(), rotation=90)
plt.show()
# Print the correlation between the seasonalities of the Government and Education & Health industries
print(____)