Compute correlations between time series

Visualizing Time Series Data in Python

Thomas Vincent

Head of Data Science, Getty Images

Trends in Jobs data

print(trend_df)

datestamp  Agriculture  Business services  Construction

2000-01-01          NaN                NaN           NaN   
2000-02-01          NaN                NaN           NaN   
2000-03-01          NaN                NaN           NaN   
2000-04-01          NaN                NaN           NaN   
2000-05-01          NaN                NaN           NaN   
2000-06-01          NaN                NaN           NaN   
2000-07-01     9.170833           4.787500      6.329167   
2000-08-01     9.466667           4.820833      6.304167   
...

Plotting a clustermap of the jobs correlation matrix

# Get correlation matrix of the seasonality_df DataFrame
trend_corr = trend_df.corr(method='spearman')

# Customize the clustermap of the seasonality_corr
correlation matrix
fig = sns.clustermap(trend_corr, annot=True, linewidth=0.4)

plt.setp(fig.ax_heatmap.yaxis.get_majorticklabels(),
rotation=0)

plt.setp(fig.ax_heatmap.xaxis.get_majorticklabels(),
rotation=90)

The jobs correlation matrix

Let's practice!

Visualizing Time Series Data in Python